Historical Contingency of Host-Microbiome Response to Perturbation

Integrated DEGxDAT Analysis Across All Research Questions

Configuration

# Configuration options for analysis
# Set to TRUE to load existing results, FALSE to recompute
LOAD_EXISTING_RESULTS <- TRUE

# Analysis parameters
top_n_taxa <- 10  # Number of top taxa to display in partial correlation summaries

# Directory to save/load results
RESULTS_DIR <- "/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results"

# Create results directory if it doesn't exist
if (!dir.exists(RESULTS_DIR)) {
  dir.create(RESULTS_DIR, recursive = TRUE)
  cat("Created results directory:", RESULTS_DIR, "\n")
}

# Function to save results with timestamp
save_results <- function(results, filename, results_dir = RESULTS_DIR) {
  timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S")
  full_filename <- file.path(results_dir, paste0(filename, "_", timestamp, ".rds"))
  saveRDS(results, full_filename)
  cat("Saved results to:", full_filename, "\n")
  return(full_filename)
}

# Function to load most recent results file
load_latest_results <- function(filename_pattern, results_dir = RESULTS_DIR) {
  files <- list.files(results_dir, pattern = filename_pattern, full.names = TRUE)
  if (length(files) == 0) {
    cat("No existing results found for pattern:", filename_pattern, "\n")
    return(NULL)
  }
  latest_file <- files[order(file.info(files)$mtime, decreasing = TRUE)[1]]
  cat("Loading results from:", latest_file, "\n")
  return(readRDS(latest_file))
}

# Display configuration
cat("=== ANALYSIS CONFIGURATION ===\n")

## === ANALYSIS CONFIGURATION ===

cat("Load existing results:", LOAD_EXISTING_RESULTS, "\n")

## Load existing results: TRUE

cat("Results directory:", RESULTS_DIR, "\n")

## Results directory: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results

cat("================================\n\n")

## ================================

Overview

This script performs integrated analysis of differentially abundant taxa (DAT) and differentially expressed genes (DEG) across three research questions:

1. Parasite Exposure Response: What is the host-microbiome response to parasite exposure? (A- T- P- vs A- T- P+)
2. Historical Contingency of Parasite Response: Is the host-microbiome response to parasite exposure historically contingent? (A- T- P- vs A+ T- P+, A- T+ P+, A+ T+ P+)
3. Historical Contingency of Recovery: Is the host-microbiome recovery historically contingent? (A- T- P- vs A+ T- P-, A- T+ P-, A+ T+ P-)

Setup

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Libraries

# Load required libraries for statistical analysis and table creation

library(gt)
library(pheatmap)
library(ggrepel)
library(igraph)
library(tidygraph)
library(ggraph)
library(DESeq2)
library(phyloseq)
library(microViz)
library(vegan)
library(RColorBrewer)
library(clusterProfiler)
library(ggpubr)
library(org.Dr.eg.db)
library(ppcor)
library(GO.db)
library(AnnotationDbi)
library(KEGGREST)
library(nptest)
library(parallel)
library(tidyverse)

Plotting

# Define treatment order and color palette
treatment_order <- c(
  "A- T- P-",  # Control
  "A- T- P+",  # Parasite
  "A+ T- P-",  # Antibiotics
  "A+ T- P+",  # Antibiotics_Parasite
  "A- T+ P-",  # Temperature
  "A- T+ P+",  # Temperature_Parasite
  "A+ T+ P-",  # Antibiotics_Temperature
  "A+ T+ P+"   # Antibiotics_Temperature_Parasite
)

# Custom color palette matching treatment order
treatment_colors <- c(
  "#1B9E77",  # A- T- P- (Control)
  "#D95F02",  # A- T- P+ (Parasite)
  "#7570B3",  # A+ T- P- (Antibiotics)
  "#E7298A",  # A+ T- P+ (Antibiotics_Parasite)
  "#66A61E",  # A- T+ P- (Temperature)
  "#E6AB02",  # A- T+ P+ (Temperature_Parasite)
  "#A6761D",  # A+ T+ P- (Antibiotics_Temperature)
  "#666666"   # A+ T+ P+ (Antibiotics_Temperature_Parasite)
)

# Create named vector for color scale
treatment_color_scale <- setNames(treatment_colors, treatment_order)

Functions

# Function to extract sample data as dataframe from phyloseq object
samdatAsDataframe <- function(ps) {
  samdat <- phyloseq::sample_data(ps)
  df <- data.frame(samdat, check.names = FALSE, stringsAsFactors = FALSE)
  return(df)
}

# Function to rename variables in phyloseq object
ps_rename <- function(ps, ...) {
  ps <- microViz::ps_get(ps)
  df <- samdatAsDataframe(ps)
  df <- dplyr::rename(.data = df, ...)
  phyloseq::sample_data(ps) <- df
  return(ps)
}

# SourceFolder function
source(here::here("Code", "R", "Functions", "StartFunctions", "sourceFolder.R"))

# Import all helper functions found in `/Functions`
sourceFolder(here::here("Code", "R", "Functions", "StartFunctions"), T)

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## 3 files sourced from folder "/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/R/Functions/StartFunctions"

sourceFolder(here::here("Code", "R", "Functions", "HelperFunctions"), T)

## 9 files sourced from folder "/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/R/Functions/HelperFunctions"

# Function to map gene IDs to Entrez IDs
get_entrez <- function(deg_df) {
  # This function maps gene symbols (from the input data frame) to Entrez IDs using org.Dr.eg.db.
  # INPUT: deg_df - a data frame with a column 'gene_id' (gene symbols)
  # OUTPUT: the input data frame with an added 'entrezgene_id' column, filtered to only those with valid Entrez IDs

  # Extract gene symbols from the input data frame
  gene_ids <- deg_df$gene_id
  
  # Map gene symbols to Entrez IDs using AnnotationDbi
  entrez_map <- AnnotationDbi::mapIds(
    org.Dr.eg.db,
    keys = gene_ids,
    column = "ENTREZID",
    keytype = "SYMBOL",
    multiVals = "first"
  ) %>%
    tibble::enframe(name = "gene_id", value = "entrezgene_id") %>%
    dplyr::filter(!is.na(entrezgene_id))
  
  # Join the Entrez IDs back to the original data, keep only those with valid Entrez IDs
  result <- deg_df %>%
    dplyr::left_join(entrez_map, by = "gene_id") %>%
    dplyr::filter(!is.na(entrezgene_id)) %>%
    dplyr::distinct(entrezgene_id, .keep_all = TRUE)
  
  return(result)
}

# Function to perform GO enrichment and summarize correlations for each GO term
get_go_correlations <- function(entrez_df) {
  # This function performs GO enrichment analysis on a set of Entrez IDs and summarizes correlation statistics for each GO term.
  # INPUT: entrez_df - a data frame with 'entrezgene_id', 'correlation', 'TaxaID', etc.
  # OUTPUT: a summary data frame with GO terms, p-values, mean correlations, and gene/taxa lists

  cat("\nProcessing contrast with", nrow(entrez_df), "genes\n")
  
  # Run GO enrichment analysis (Biological Process ontology)
  go_results <- enrichGO(
    gene = entrez_df$entrezgene_id,
    OrgDb = org.Dr.eg.db,
    keyType = "ENTREZID",
    ont = "BP",
    pAdjustMethod = "BH",
    pvalueCutoff = 0.1,
    qvalueCutoff = 0.2,
    readable = TRUE
  ) %>%
    as.data.frame() %>%
    dplyr::select(ID, Description, p.adjust, Count, geneID) %>%
    dplyr::mutate(log_padj = -log10(p.adjust))
  
  # Get GO term categories and definitions for each GO term
  go_categories <- AnnotationDbi::select(
    GO.db,
    keys = go_results$ID,
    columns = c("GOID", "TERM", "ONTOLOGY", "DEFINITION"),
    keytype = "GOID"
  )
  
  # For each GO term, get its parent terms (hierarchy)
  go_parents <- lapply(go_results$ID, function(go_id) {
    parents <- GO.db::GOBPANCESTOR[[go_id]]
    if (is.null(parents)) return(NA)
    parent_terms <- AnnotationDbi::select(
      GO.db,
      keys = parents,
      columns = c("GOID", "TERM"),
      keytype = "GOID"
    )
    return(list(parent_terms$TERM))
  })
  
  # Create a mapping of GO IDs to their parent terms
  go_hierarchy <- tibble::tibble(
    GOID = go_results$ID,
    parent_terms = go_parents
  )
  
  # Map Entrez IDs to gene symbols for easier interpretation
  symbol_to_entrez <- AnnotationDbi::mapIds(
    org.Dr.eg.db,
    keys = entrez_df$entrezgene_id,
    column = "SYMBOL",
    keytype = "ENTREZID",
    multiVals = "first"
  ) %>%
    tibble::enframe(name = "entrezgene_id", value = "SYMBOL") %>%
    dplyr::filter(!is.na(SYMBOL))
  
  # Prepare a mapping of gene IDs to correlation and taxa information
  gene_cor_map <- entrez_df %>%
    dplyr::select(entrezgene_id, correlation, TaxaID, abs_correlation) %>%
    dplyr::left_join(symbol_to_entrez, by = "entrezgene_id") %>%
    dplyr::select(SYMBOL, correlation, TaxaID, abs_correlation) %>%
    dplyr::filter(!is.na(SYMBOL))
  
  # Expand the geneID column (GO term members) and join with correlation/taxa info
  go_results_long <- go_results %>%
    tidyr::separate_rows(geneID, sep = "/") %>%
    dplyr::left_join(gene_cor_map, by = c("geneID" = "SYMBOL")) %>%
    dplyr::left_join(go_categories, by = c("ID" = "GOID")) %>%
    dplyr::left_join(go_hierarchy, by = c("ID" = "GOID"))
  
  # Summarize by GO term: mean correlation, number of positive/negative, gene/taxa lists, etc.
  go_results_summary <- go_results_long %>%
    dplyr::group_by(ID, Description, TERM, ONTOLOGY, DEFINITION, parent_terms, p.adjust, Count, log_padj) %>%
    dplyr::summarise(
      mean_correlation = mean(correlation, na.rm = TRUE),
      mean_abs_correlation = mean(abs_correlation, na.rm = TRUE),
      n_positive = sum(correlation > 0, na.rm = TRUE),
      n_negative = sum(correlation < 0, na.rm = TRUE),
      gene_list = list(geneID),
      correlation_list = list(correlation),
      taxa_list = list(TaxaID),
      .groups = "drop"
    ) %>%
    dplyr::arrange(p.adjust)
  
  return(go_results_summary)
}

# Function to enrich KEGG pathways for a set of significant gene-taxa correlations
get_kegg_pathways <- function(sig_partial_cor) {
  # This function performs KEGG pathway enrichment for a set of significant gene-taxa correlations.
  # INPUT: sig_partial_cor - data frame with at least 'gene_id' (gene symbol)
  # OUTPUT: summary table of enriched KEGG pathways, with gene/taxa/correlation info for each pathway

  cat("\nProcessing KEGG analysis for significant correlations\n")
  
  # Get unique gene symbols from significant correlations
  sig_genes <- unique(sig_partial_cor$gene_id)
  cat("Number of significant genes:", length(sig_genes), "\n")
  
  # Map gene symbols to Entrez IDs
  entrez_ids <- AnnotationDbi::mapIds(
    org.Dr.eg.db,
    keys = sig_genes,
    column = "ENTREZID",
    keytype = "SYMBOL",
    multiVals = "first"
  ) %>%
    tibble::enframe(name = "SYMBOL", value = "ENTREZID") %>%
    dplyr::filter(!is.na(ENTREZID))
  
  cat("Number of genes successfully mapped to Entrez IDs:", nrow(entrez_ids), "\n")
  
  # Run KEGG enrichment analysis
  kegg_results <- enrichKEGG(
    gene = entrez_ids$ENTREZID,
    organism = 'dre',  # Danio rerio
    keyType = 'ncbi-geneid',
    pAdjustMethod = "BH",
    pvalueCutoff = .1,
    qvalueCutoff = .2
  )
  
  if (is.null(kegg_results)) {
    cat("No KEGG pathways found.\n")
    return(NULL)
  }
  
  kegg_results <- as.data.frame(kegg_results) %>%
    dplyr::select(ID, Description, p.adjust, Count, geneID) %>%
    dplyr::mutate(log_padj = -log10(p.adjust))
  
  # For each pathway, get additional info (name, description, class)
  kegg_info <- lapply(kegg_results$ID, function(path_id) {
    clean_id <- gsub("dre", "", path_id)
    tryCatch({
      path_info <- KEGGREST::keggGet(clean_id)
      if (length(path_info) > 0) {
        return(list(
          name = path_info[[1]]$NAME,
          description = ifelse(is.null(path_info[[1]]$DESCRIPTION),
                             path_info[[1]]$NAME,
                             path_info[[1]]$DESCRIPTION),
          class = ifelse(is.null(path_info[[1]]$CLASS),
                        "Not specified",
                        path_info[[1]]$CLASS)
        ))
      }
    }, error = function(e) {
      tryCatch({
        path_info <- KEGGREST::keggGet(path_id)
        if (length(path_info) > 0) {
          return(list(
            name = path_info[[1]]$NAME,
            description = ifelse(is.null(path_info[[1]]$DESCRIPTION),
                               path_info[[1]]$NAME,
                               path_info[[1]]$DESCRIPTION),
            class = ifelse(is.null(path_info[[1]]$CLASS),
                          "Not specified",
                          path_info[[1]]$CLASS)
          ))
        }
      }, error = function(e2) {
        return(list(
          name = "Not available",
          description = "Not available",
          class = "Not available"
        ))
      })
    })
  })
  
  # Create a mapping of KEGG IDs to their information
  kegg_details <- tibble::tibble(
    KEGGID = kegg_results$ID,
    Name = sapply(kegg_info, function(x) x$name),
    Description = sapply(kegg_info, function(x) x$description),
    Class = sapply(kegg_info, function(x) x$class)
  )
  
  # Map Entrez IDs to gene symbols
  id_mapping <- entrez_ids %>%
    dplyr::select(ENTREZID, SYMBOL)
  
  # Expand geneID column and join with gene symbol mapping
  kegg_results_long <- kegg_results %>%
    tidyr::separate_rows(geneID, sep = "/") %>%
    dplyr::mutate(geneID = as.character(geneID)) %>%
    dplyr::left_join(id_mapping, by = c("geneID" = "ENTREZID"))

  # For each pathway, summarize gene-taxa-correlation relationships
  kegg_summary <- kegg_results_long %>%
    dplyr::group_by(ID) %>%
    dplyr::summarise(
      Name = Description[1],
      Description = kegg_details$Description[kegg_details$KEGGID == ID[1]][1],
      Class = kegg_details$Class[kegg_details$KEGGID == ID[1]][1],
      p.adjust = p.adjust[1],
      Count = Count[1],
      Gene_List = list(unique(SYMBOL)),
      .groups = "drop"
    ) %>%
    dplyr::mutate(
      Gene_Taxa_Relations = lapply(Gene_List, function(genes) {
        sig_partial_cor %>%
          dplyr::filter(gene_id %in% genes) %>%
          dplyr::select(gene_id, TaxaID, correlation) %>%
          dplyr::arrange(gene_id, TaxaID)
      }),
      Taxa_List = lapply(Gene_Taxa_Relations, function(rel) {
        unique(rel$TaxaID)
      }),
      Correlation_List = lapply(Gene_Taxa_Relations, function(rel) {
        rel$correlation
      })
    ) %>%
    dplyr::arrange(p.adjust)

  return(kegg_summary)
}

# Helper function to get top correlations by correlation, taxa, or gene
gen_get_top_correlations <- function(correlation_results, top_n = 100, top_by = c("correlation", "taxa", "gene")) {
  top_by <- match.arg(top_by)
  
  # Check if we have any significant correlations
  sig_correlations <- correlation_results %>%
    dplyr::filter(fdr < 0.05)
  
  if(nrow(sig_correlations) == 0) {
    cat("WARNING: No significant correlations found (FDR < 0.05). Returning empty data frame.\n")
    return(data.frame())
  }
  
  if (top_by == "correlation") {
    top_correlations <- sig_correlations %>%
      dplyr::arrange(desc(abs_correlation)) %>%
      head(top_n)
  } else if (top_by == "taxa") {
    top_taxa <- sig_correlations %>%
      dplyr::group_by(TaxaID) %>%
      dplyr::summarise(n = n()) %>%
      dplyr::arrange(desc(n)) %>%
      head(top_n) %>%
      dplyr::pull(TaxaID)
    
    if(length(top_taxa) == 0) {
      cat("WARNING: No taxa found with significant correlations. Returning empty data frame.\n")
      return(data.frame())
    }
    
    top_correlations <- sig_correlations %>%
      dplyr::filter(TaxaID %in% top_taxa)
  } else if (top_by == "gene") {
    top_genes <- sig_correlations %>%
      dplyr::group_by(gene_id) %>%
      dplyr::summarise(n = n()) %>%
      dplyr::arrange(desc(n)) %>%
      head(top_n) %>%
      dplyr::pull(gene_id)
    
    if(length(top_genes) == 0) {
      cat("WARNING: No genes found with significant correlations. Returning empty data frame.\n")
      return(data.frame())
    }
    
    top_correlations <- sig_correlations %>%
      dplyr::filter(gene_id %in% top_genes)
  }
  
  return(top_correlations)
}

# Function to perform integrated analysis for a single research question
perform_integrated_analysis <- function(question_name, 
                                       dat_results, 
                                       deg_results, 
                                       taxa_counts, 
                                       expr_counts, 
                                       metadata, 
                                       treatment_comparison,
                                       top_n = 100,
                                       top_by = c("correlation", "taxa", "gene")) {
  # This function performs the full DEGxDAT analysis for a single research question (single treatment comparison).
  # INPUTS:
  #   - question_name: string, name of the research question
  #   - dat_results: data frame of DAT results
  #   - deg_results: data frame of DEG results
  #   - taxa_counts: OTU/taxa count matrix
  #   - expr_counts: gene expression count matrix
  #   - metadata: sample metadata
  #   - treatment_comparison: vector of treatment names to compare
  #   - top_n: number of top correlations to use for partial correlation analysis
  #   - top_by: selection of top correlations by correlation, taxa, or gene
  # OUTPUT: list with all results for this comparison (correlations, significant genes/taxa, etc.)

  cat("\n=== ANALYSIS FOR:", question_name, "===\n")
  cat("Treatment comparison:", paste(treatment_comparison, collapse = " vs "), "\n")
  
  # Set seed for reproducibility
  set.seed(42)
  
  # Filter for significant DEGs and DATs
  deg_sig <- deg_results %>%
    dplyr::filter(padj < 0.05)
  
  dat_sig <- dat_results %>%
    dplyr::filter(qval < 0.05)
  
  cat("Significant genes:", nrow(deg_sig), "\n")
  cat("Significant taxa:", nrow(dat_sig), "\n")
  
  # Check if we have any significant genes or taxa
  if(nrow(deg_sig) == 0 || nrow(dat_sig) == 0) {
    cat("WARNING: No significant genes or taxa found. Returning empty results.\n")
    return(list(
      question_name = question_name,
      treatment_comparison = treatment_comparison,
      correlation_results = data.frame(),
      sig_correlations = data.frame(),
      partial_cor_results = data.frame(),
      sig_partial_correlations = data.frame(),
      failed_pairs = list(),
      deg_sig = deg_sig,
      dat_sig = dat_sig,
      samples = character(0)
    ))
  }
  
  # Get relevant samples for this comparison
  rna_samples <- metadata %>%
    dplyr::filter(Treatment %in% treatment_comparison) %>%
    dplyr::filter(Sample %in% colnames(expr_counts)) %>%
    dplyr::pull(Sample) %>%
    as.character()
  
  cat("Samples for analysis:", length(rna_samples), "\n")
  
  # Check if we have enough samples
  if(length(rna_samples) < 3) {
    cat("WARNING: Insufficient samples for analysis. Returning empty results.\n")
    return(list(
      question_name = question_name,
      treatment_comparison = treatment_comparison,
      correlation_results = data.frame(),
      sig_correlations = data.frame(),
      partial_cor_results = data.frame(),
      sig_partial_correlations = data.frame(),
      failed_pairs = list(),
      deg_sig = deg_sig,
      dat_sig = dat_sig,
      samples = rna_samples
    ))
  }
  
  # Filter expression data for significant genes and relevant samples
  filtered_expr <- expr_counts %>%
    dplyr::filter(gene %in% deg_sig$gene) %>%
    dplyr::select(gene, gene_id, gene_name, any_of(rna_samples))
  
  # Filter taxa data for significant taxa and relevant samples
  filtered_taxa <- taxa_counts %>%
    as.data.frame() %>%
    tibble::rownames_to_column(var = "Sample") %>%
    dplyr::mutate(Sample = gsub("^f", "", Sample)) %>%
    dplyr::select(Sample, any_of(dat_sig$taxa)) %>%
    dplyr::filter(Sample %in% rna_samples)
  
  # Check if we have data after filtering
  if(nrow(filtered_expr) == 0 || ncol(filtered_taxa) <= 1) {
    cat("WARNING: No expression or taxa data available after filtering. Returning empty results.\n")
    return(list(
      question_name = question_name,
      treatment_comparison = treatment_comparison,
      correlation_results = data.frame(),
      sig_correlations = data.frame(),
      partial_cor_results = data.frame(),
      sig_partial_correlations = data.frame(),
      failed_pairs = list(),
      deg_sig = deg_sig,
      dat_sig = dat_sig,
      samples = rna_samples
    ))
  }
  
  # Normalize gene expression data (z-score per gene)
  normalized_expr <- filtered_expr %>%
    tidyr::pivot_longer(
      cols = -c(gene, gene_id, gene_name),
      names_to = "Sample",
      values_to = "count"
    ) %>%
    dplyr::group_by(gene_id) %>%
    dplyr::mutate(
      z_score = (count - mean(count)) / sd(count)
    ) %>%
    dplyr::select(-count) %>%
    tidyr::pivot_wider(
      names_from = Sample,
      values_from = z_score
    )
  
  # Normalize taxa data (z-score per taxon)
  prepared_taxa <- filtered_taxa %>%
    tidyr::pivot_longer(
      cols = -Sample,
      names_to = "TaxaID",
      values_to = "abundance"
    ) %>%
    dplyr::group_by(TaxaID) %>%
    dplyr::mutate(
      z_score = (abundance - mean(abundance)) / sd(abundance)
    ) %>%
    dplyr::select(Sample, TaxaID, z_score) %>%
    dplyr::rename(abundance = z_score) %>%
    dplyr::mutate(Sample = as.character(Sample))
  
  # Prepare expression data for correlation
  prepared_expr <- normalized_expr %>%
    tidyr::pivot_longer(
      cols = -c(gene, gene_id, gene_name),
      names_to = "Sample",
      values_to = "z_score"
    )
  
  # Calculate gene-taxa correlations (Spearman)
  correlation_results <- prepared_expr %>%
    dplyr::left_join(
      prepared_taxa,
      by = "Sample"
    ) %>%
    dplyr::group_by(gene_id, gene_name, TaxaID) %>%
    dplyr::summarise(
      correlation = cor(z_score, abundance, method = "spearman"),
      .groups = "drop"
    ) %>%
    dplyr::mutate(
      abs_correlation = abs(correlation)
    ) %>%
    dplyr::arrange(desc(abs_correlation))
  
  # Calculate significance (p-value, FDR)
  correlation_results <- correlation_results %>%
    dplyr::mutate(
      p_value = 2 * (1 - pnorm(abs(correlation) * sqrt((nrow(filtered_taxa) - 2) / (1 - correlation^2)))),
      fdr = p.adjust(p_value, method = "BH")
    )
  
  # Get significant correlations (FDR < 0.05)
  sig_correlations <- correlation_results %>%
    dplyr::filter(fdr < 0.05) %>%
    dplyr::arrange(desc(abs_correlation))
  
  cat("Significant correlations:", nrow(sig_correlations), "\n")
  
  # Check if we have any significant correlations before proceeding with partial correlation
  if(nrow(sig_correlations) == 0) {
    cat("WARNING: No significant correlations found. Skipping partial correlation analysis.\n")
    return(list(
      question_name = question_name,
      treatment_comparison = treatment_comparison,
      correlation_results = correlation_results,
      sig_correlations = sig_correlations,
      partial_cor_results = data.frame(),
      sig_partial_correlations = data.frame(),
      failed_pairs = list(),
      deg_sig = deg_sig,
      dat_sig = dat_sig,
      samples = rna_samples
    ))
  }
  
  # Get top gene-taxa pairs for partial correlation based on user selection
  top_correlations <- gen_get_top_correlations(correlation_results, top_n = top_n, top_by = top_by)
  
  # Check if we have any top correlations
  if(nrow(top_correlations) == 0) {
    cat("WARNING: No top correlations found for partial correlation analysis.\n")
    return(list(
      question_name = question_name,
      treatment_comparison = treatment_comparison,
      correlation_results = correlation_results,
      sig_correlations = sig_correlations,
      partial_cor_results = data.frame(),
      sig_partial_correlations = data.frame(),
      failed_pairs = list(),
      deg_sig = deg_sig,
      dat_sig = dat_sig,
      samples = rna_samples
    ))
  }
  
  cat(paste0("Top ", nrow(top_correlations), " by ", top_by, " for partial correlation:\n"))
  print(top_correlations %>% dplyr::select(gene_id, TaxaID, abs_correlation))
  
  # Only run partial correlation on these gene-taxa pairs
  top_pairs <- top_correlations %>%
    dplyr::select(gene_id, TaxaID) %>%
    dplyr::distinct()
  
  # Check if we have valid gene-taxa pairs
  if(nrow(top_pairs) == 0 || any(is.na(top_pairs$gene_id)) || any(is.na(top_pairs$TaxaID))) {
    cat("WARNING: No valid gene-taxa pairs found for partial correlation analysis.\n")
    return(list(
      question_name = question_name,
      treatment_comparison = treatment_comparison,
      correlation_results = correlation_results,
      sig_correlations = sig_correlations,
      partial_cor_results = data.frame(),
      sig_partial_correlations = data.frame(),
      failed_pairs = list(),
      deg_sig = deg_sig,
      dat_sig = dat_sig,
      samples = rna_samples
    ))
  }
  
  # Perform partial correlation analysis (controlling for worm counts)
  cat("Performing partial correlation analysis...\n")
  
  # Convert filtered expression data to wide format with samples as rows
  expr_wide <- filtered_expr %>%
    tidyr::pivot_longer(
      cols = -c(gene, gene_id, gene_name),
      names_to = "Sample",
      values_to = "count"
    ) %>%
    dplyr::group_by(gene_id) %>%
    dplyr::mutate(
      z_score = (count - mean(count)) / sd(count)
    ) %>%
    dplyr::select(-c(count, gene_name, gene)) %>%
    tidyr::pivot_wider(
      names_from = gene_id,
      values_from = z_score
    ) %>%
    dplyr::arrange(as.numeric(Sample))
  
  # Convert filtered taxa data to wide format with samples as rows
  taxa_wide <- filtered_taxa %>%
    tidyr::pivot_longer(
      cols = -Sample,
      names_to = "TaxaID",
      values_to = "abundance"
    ) %>%
    tidyr::pivot_wider(
      names_from = TaxaID,
      values_from = abundance
    ) %>%
    dplyr::mutate(Sample = as.character(Sample)) %>%
    dplyr::arrange(as.numeric(Sample))
  
  # Create control variable matrix (worm counts)
  control_matrix <- metadata %>%
    dplyr::filter(Sample %in% rna_samples) %>%
    dplyr::arrange(Sample) %>%
    dplyr::select(Total.Worm.Count) %>%
    as.matrix()
  
  # Verify sample alignment
  if(!all(expr_wide$Sample == taxa_wide$Sample) || 
     !all(expr_wide$Sample == rownames(control_matrix))) {
    stop("Sample order mismatch between expression, taxa, and control data")
  }
  
  # Initialize results storage for partial correlations
  partial_cor_results <- list()
  failed_pairs <- list()
  
  # Set up parallel processing
  library(parallel)
  cl <- makeCluster(6)  # Create a cluster with 6 cores
  on.exit(stopCluster(cl))  # Ensure cluster is stopped when done
  
  # Calculate partial correlations for the top pairs
  for(i in 1:nrow(top_pairs)) {
    gene <- top_pairs$gene_id[i]
    taxa <- top_pairs$TaxaID[i]
    
    # Skip if gene or taxa is NA
    if(is.na(gene) || is.na(taxa)) {
      failed_pairs[[paste(gene, taxa, sep = "_")]] <- "NA gene or taxa ID"
      next
    }
    
    # Check if gene and taxa exist in the data
    if(!gene %in% colnames(expr_wide) || !taxa %in% colnames(taxa_wide)) {
      failed_pairs[[paste(gene, taxa, sep = "_")]] <- "Gene or taxa not found in data"
      next
    }
    
    # Get data for this gene-taxa pair
    x <- expr_wide[[gene]]
    y <- taxa_wide[[taxa]]
    
    # Skip if any data is missing
    if(any(is.na(c(x, y)))) {
      failed_pairs[[paste(gene, taxa, sep = "_")]] <- "Missing data"
      next
    }
    
    set.seed(42)
    
    # Calculate partial correlation
    res_pcor <- try(nptest::np.cor.test(
      x = x,
      y = y,
      z = control_matrix,
      partial = TRUE,
      parallel = TRUE,
      cl = cl,
      R = 1000,
      na.rm = TRUE
    ), silent = TRUE)
    
    # Only store results if calculation was successful
    if(!inherits(res_pcor, "try-error")) {
      partial_cor_results[[paste(gene, taxa, sep = "_")]] <- list(
        gene_id = gene,
        taxa_id = taxa,
        correlation = res_pcor$estimate,
        p_value = res_pcor$p.value
      )
    } else {
      failed_pairs[[paste(gene, taxa, sep = "_")]] <- as.character(res_pcor)
    }
  }
  
  # Convert partial correlation results to data frame
  if(length(partial_cor_results) > 0) {
    partial_cor_df <- do.call(rbind, lapply(partial_cor_results, function(x) {
      data.frame(
        gene_id = x$gene_id,
        taxa_id = x$taxa_id,
        correlation = x$correlation,
        p_value = x$p_value
      )
    })) %>%
      dplyr::mutate(
        fdr = p.adjust(p_value, method = "BH"),
        abs_correlation = abs(correlation)
      ) %>%
      dplyr::arrange(desc(abs_correlation))
    
    # Get significant partial correlations (FDR < 0.1)
    sig_partial_cor <- partial_cor_df %>%
      dplyr::filter(fdr < 0.1) %>%
      dplyr::arrange(desc(abs_correlation))
    
    cat("Significant partial correlations (FDR < 0.1):", nrow(sig_partial_cor), "\n")
  } else {
    partial_cor_df <- data.frame()
    sig_partial_cor <- data.frame()
    cat("No successful partial correlations were calculated.\n")
  }
  
  # Return all results for this comparison
  return(list(
    question_name = question_name,
    treatment_comparison = treatment_comparison,
    correlation_results = correlation_results,
    sig_correlations = sig_correlations,
    partial_cor_results = partial_cor_df,
    sig_partial_correlations = sig_partial_cor,
    failed_pairs = failed_pairs,
    deg_sig = deg_sig,
    dat_sig = dat_sig,
    samples = rna_samples
  ))
}

# Function to perform all pairwise comparisons for a research question
perform_pairwise_analysis <- function(question_name, 
                                     dat_results, 
                                     deg_results, 
                                     taxa_counts, 
                                     expr_counts, 
                                     metadata, 
                                     base_treatment,
                                     comparison_treatments,
                                     top_n = 100,
                                     top_by = c("correlation", "taxa", "gene")) {
  # This function runs DEGxDAT analysis for all pairwise comparisons between a base treatment and a set of comparison treatments.
  # INPUTS:
  #   - question_name: string, name of the research question
  #   - dat_results, deg_results, taxa_counts, expr_counts, metadata: as above
  #   - base_treatment: string, the reference treatment
  #   - comparison_treatments: vector of treatments to compare to base
  # OUTPUT: list with all pairwise results and combined summary tables

  cat("\n=== PAIRWISE ANALYSIS FOR:", question_name, "===\n")
  cat("Base treatment:", base_treatment, "\n")
  cat("Comparison treatments:", paste(comparison_treatments, collapse = ", "), "\n")
  
  # Store results for each pairwise comparison
  pairwise_results <- list()
  
  # Loop over each comparison treatment
  for(i in seq_along(comparison_treatments)) {
    comparison_treatment <- comparison_treatments[i]
    comparison_name <- paste(base_treatment, "vs", comparison_treatment)
    
    cat("\n--- Comparison", i, ":", comparison_name, "---\n")
    
    # Create treatment comparison vector
    treatment_comparison <- c(base_treatment, comparison_treatment)
    
    # Run integrated analysis for this pair
    result <- perform_integrated_analysis(
      question_name = comparison_name,
      dat_results = dat_results,
      deg_results = deg_results,
      taxa_counts = taxa_counts,
      expr_counts = expr_counts,
      metadata = metadata,
      treatment_comparison = treatment_comparison,
      top_n = top_n,
      top_by = top_by
    )
    
    # Add comparison info to result
    result$comparison_name <- comparison_name
    result$base_treatment <- base_treatment
    result$comparison_treatment <- comparison_treatment
    
    pairwise_results[[comparison_name]] <- result
  }
  
  # Combine significant correlations from all pairs (handle empty data frames)
  combined_correlations <- do.call(rbind, lapply(pairwise_results, function(x) {
    if(nrow(x$sig_correlations) > 0) {
      x$sig_correlations %>%
        dplyr::mutate(
          comparison_name = x$comparison_name,
          base_treatment = x$base_treatment,
          comparison_treatment = x$comparison_treatment
        )
    } else {
      data.frame()
    }
  }))
  
  # Combine all correlations from all pairs (handle empty data frames)
  combined_all_correlations <- do.call(rbind, lapply(pairwise_results, function(x) {
    if(nrow(x$correlation_results) > 0) {
      x$correlation_results %>%
        dplyr::mutate(
          comparison_name = x$comparison_name,
          base_treatment = x$base_treatment,
          comparison_treatment = x$comparison_treatment
        )
    } else {
      data.frame()
    }
  }))
  
  # Combine partial correlation results from all pairs
  combined_partial_correlations <- do.call(rbind, lapply(pairwise_results, function(x) {
    if(nrow(x$partial_cor_results) > 0) {
      x$partial_cor_results %>%
        dplyr::mutate(
          comparison_name = x$comparison_name,
          base_treatment = x$base_treatment,
          comparison_treatment = x$comparison_treatment
        )
    } else {
      data.frame()
    }
  }))
  
  # Combine significant partial correlations from all pairs
  combined_sig_partial_correlations <- do.call(rbind, lapply(pairwise_results, function(x) {
    if(nrow(x$sig_partial_correlations) > 0) {
      x$sig_partial_correlations %>%
        dplyr::mutate(
          comparison_name = x$comparison_name,
          base_treatment = x$base_treatment,
          comparison_treatment = x$comparison_treatment
        )
    } else {
      data.frame()
    }
  }))
  
  # Combine significant DEGs and DATs from all pairs (handle empty data frames)
  combined_deg <- do.call(rbind, lapply(pairwise_results, function(x) {
    if(nrow(x$deg_sig) > 0) {
      x$deg_sig %>%
        dplyr::mutate(
          comparison_name = x$comparison_name,
          base_treatment = x$base_treatment,
          comparison_treatment = x$comparison_treatment
        )
    } else {
      data.frame()
    }
  }))
  
  combined_dat <- do.call(rbind, lapply(pairwise_results, function(x) {
    if(nrow(x$dat_sig) > 0) {
      x$dat_sig %>%
        dplyr::mutate(
          comparison_name = x$comparison_name,
          base_treatment = x$base_treatment,
          comparison_treatment = x$comparison_treatment
        )
    } else {
      data.frame()
    }
  }))
  
  # Get all unique samples used in any pairwise comparison
  all_samples <- unique(unlist(lapply(pairwise_results, function(x) x$samples)))
  
  # Print summary of results
  cat("\n=== PAIRWISE ANALYSIS SUMMARY ===\n")
  cat("Total comparisons completed:", length(pairwise_results), "\n")
  cat("Comparisons with significant correlations:", sum(sapply(pairwise_results, function(x) nrow(x$sig_correlations) > 0)), "\n")
  cat("Comparisons with significant partial correlations:", sum(sapply(pairwise_results, function(x) nrow(x$sig_partial_correlations) > 0)), "\n")
  cat("Total significant correlations:", nrow(combined_correlations), "\n")
  cat("Total significant partial correlations:", nrow(combined_sig_partial_correlations), "\n")
  
  return(list(
    question_name = question_name,
    base_treatment = base_treatment,
    comparison_treatments = comparison_treatments,
    pairwise_results = pairwise_results,
    combined_correlations = combined_correlations,
    combined_all_correlations = combined_all_correlations,
    combined_partial_correlations = combined_partial_correlations,
    combined_sig_partial_correlations = combined_sig_partial_correlations,
    combined_deg = combined_deg,
    combined_dat = combined_dat,
    all_samples = all_samples
  ))
}

Import Data

Phyloseq

ps.tmp <- readRDS("/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Data/Robjects/pseq_uncleaned_05052025.rds")

ps.cleaned <-
    ps.tmp %>%
        ## Update Metadata
        ps_rename(Time = Timepoint) %>%
        microViz::ps_mutate(
            Treatment = case_when(
                Antibiotics == 0 & Temperature == 0 & Pathogen == 0 ~ "A- T- P-",
                Antibiotics == 0 & Temperature == 0 & Pathogen == 1 ~ "A- T- P+",
                Antibiotics == 1 & Temperature == 0 & Pathogen == 0 ~ "A+ T- P-",
                Antibiotics == 1 & Temperature == 0 & Pathogen == 1 ~ "A+ T- P+",
                Antibiotics == 0 & Temperature == 1 & Pathogen == 0 ~ "A- T+ P-",
                Antibiotics == 0 & Temperature == 1 & Pathogen == 1 ~ "A- T+ P+",
                Antibiotics == 1 & Temperature == 1 & Pathogen == 0 ~ "A+ T+ P-",
                Antibiotics == 1 & Temperature == 1 & Pathogen == 1 ~ "A+ T+ P+",
                TRUE ~ "Unknown"
            ), .after = "Pathogen"
        ) %>%
        microViz::ps_mutate(Sample = fecal.sample.number, .before = 1) %>%
        microViz::ps_mutate(Sample = gsub("^f", "", Sample)) %>%
        microViz::ps_filter(Treatment != "Unknown") %>%
        microViz::ps_mutate(
            History = case_when(
                Antibiotics + Temperature == 0 ~ 0,
                Antibiotics + Temperature == 1 ~ 1,
                Antibiotics + Temperature == 2 ~ 2,
            ), .after = "Treatment"
        ) %>%
        
        ## Additional metadata updates, factorizing metadata
        microViz::ps_mutate(
        # Create treatment code
            treatment_code = case_when(
              Antibiotics == 0 & Temperature == 0 & Pathogen == 0 ~ "Aneg_Tneg_Pneg",
              Antibiotics == 0 & Temperature == 0 & Pathogen == 1 ~ "Aneg_Tneg_Ppos",
              Antibiotics == 1 & Temperature == 0 & Pathogen == 0 ~ "Apos_Tneg_Pneg",
              Antibiotics == 1 & Temperature == 0 & Pathogen == 1 ~ "Apos_Tneg_Ppos",
              Antibiotics == 0 & Temperature == 1 & Pathogen == 0 ~ "Aneg_Tpos_Pneg",
              Antibiotics == 0 & Temperature == 1 & Pathogen == 1 ~ "Aneg_Tpos_Ppos",
              Antibiotics == 1 & Temperature == 1 & Pathogen == 0 ~ "Apos_Tpos_Pneg",
              Antibiotics == 1 & Temperature == 1 & Pathogen == 1 ~ "Apos_Tpos_Ppos"
            ),
            # Create treatment group factor
            treatment_group = case_when(
              Antibiotics == 0 & Temperature == 0 & Pathogen == 1 ~ "Parasite",
              Antibiotics == 1 & Temperature == 0 & Pathogen == 0 ~ "Antibiotics",
              Antibiotics == 1 & Temperature == 0 & Pathogen == 1 ~ "Antibiotics_Parasite",
              Antibiotics == 0 & Temperature == 1 & Pathogen == 0 ~ "Temperature",
              Antibiotics == 0 & Temperature == 1 & Pathogen == 1 ~ "Temperature_Parasite",
              Antibiotics == 1 & Temperature == 1 & Pathogen == 0 ~ "Antibiotics_Temperature",
              Antibiotics == 1 & Temperature == 1 & Pathogen == 1 ~ "Antibiotics_Temperature_Parasite",
              TRUE ~ "Control"
            ),
            # Convert to factor with appropriate levels
            treatment_group = factor(treatment_group, 
                                   levels = c("Control", "Parasite", 
                                              "Antibiotics", "Antibiotics_Parasite",
                                              "Temperature", "Temperature_Parasite",
                                            "Antibiotics_Temperature", "Antibiotics_Temperature_Parasite")
                                   ),
            treatment_code = factor(treatment_code, levels = treatment_order),
            # Create time point factor
            time_point = factor(Time, levels = c(0, 14, 18, 25, 29, 60)),
            # Create pathogen status factor
            pathogen_status = factor(ifelse(Pathogen == 1, "Exposed", "Unexposed"),
                                   levels = c("Unexposed", "Exposed")),
            # Create sex factor
            sex = factor(Sex, levels = c("M", "F"))
            )  %>%
    microViz::ps_mutate(Treatment = factor(Treatment, levels = treatment_order)) %>%
      microViz::ps_mutate(Exp_Type = case_when(
          Treatment %in% c("A- T- P-", "A- T- P+")  ~ "No prior stressor(s)",
          Treatment %in% c("A+ T- P-", "A+ T- P+")  ~ "Antibiotics",
          Treatment %in% c("A- T+ P-", "A- T+ P+") ~ "Temperature",
          Treatment %in% c("A+ T+ P-", "A+ T+ P+") ~ "Combined",
      )) %>%
      microViz::ps_mutate(Exp_Type = factor(Exp_Type, levels = c("No prior stressor(s)", "Antibiotics", "Temperature", "Combined"))) %>%
  # Fix names for taxonomic ranks not identified
  microViz::tax_fix(suffix_rank = "current", anon_unique = T, unknown = NA) %>% 
  # Filter for any samples that contain more than 5000 reads
  microViz::ps_filter(sample_sums(.) > 5000) %>%
  # Any taxa not found in at least 3 samples are removed
  microViz::tax_filter(min_prevalence = 3, undetected = 0) %>%
  # Remove any unwanted reads
  microViz::tax_select(c("Mitochondria", "Chloroplast", "Eukaryota"), deselect = TRUE) %>%
  microViz::tax_select(c("Bacteria, Phylum"), deselect = TRUE) 

# Read and process OTU table for all treatments
taxa_counts.all <- ps.cleaned %>%
    microViz::tax_agg(rank = "Genus") %>%
    microViz::otu_get()

# Create taxa counts for different treatment comparisons
taxa_counts.parasite <- ps.cleaned %>%
    microViz::ps_filter(Treatment %in% c("A- T- P-", "A- T- P+")) %>%
    microViz::tax_agg(rank = "Genus") %>%
    microViz::otu_get()

taxa_counts.priorStressors <- ps.cleaned %>%
    microViz::ps_filter(Treatment %in% c("A- T- P+", "A+ T- P+", "A- T+ P+", "A+ T+ P+")) %>%
    microViz::tax_agg(rank = "Genus") %>%
    microViz::otu_get()

taxa_counts.noPriorStressors <- ps.cleaned %>%
    microViz::ps_filter(Treatment %in% c("A- T- P-", "A+ T- P-", "A- T+ P-", "A+ T+ P-")) %>%
    microViz::tax_agg(rank = "Genus") %>%
    microViz::otu_get()

DAT Results

# Question 0: All
dat_results.all <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffAbund/Results/All_60DPE__TREATMENT__significant_results.tsv') %>%
    dplyr::rename(taxa = feature)

## Rows: 610 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): feature, metadata, value
## dbl (6): coef, stderr, N, N.not.0, pval, qval
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Question 1: Parasite Exposure Response
dat_results.parasite <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffAbund/Results/ParasiteExp_60DPE__TREATMENT__significant_results.tsv') %>%
    dplyr::rename(taxa = feature)

## Rows: 76 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): feature, metadata, value
## dbl (6): coef, stderr, N, N.not.0, pval, qval
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Question 2: Historical Contingency of Parasite Response
dat_results.historical_contingency <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffAbund/Results/PriorStressParaExp_60DPE__TREATMENT__significant_results.tsv') %>%
    dplyr::rename(taxa = feature)

## Rows: 97 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): feature, metadata, value
## dbl (6): coef, stderr, N, N.not.0, pval, qval
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Question 3: Historical Contingency of Recovery
dat_results.recovery <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffAbund/Results/PriorStressNoParaExp_60DPE__TREATMENT__significant_results.tsv') %>%
    dplyr::rename(taxa = feature)

## Rows: 196 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): feature, metadata, value
## dbl (6): coef, stderr, N, N.not.0, pval, qval
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

DEG Results

# Question 0: All

deg_results.all <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffExpGene/Results/All_Treatments__significant_results.tsv')

## Rows: 21638 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): gene, metadata, value
## dbl (5): log2FoldChange, padj, baseMean, pvalue, stat
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Question 1: Parasite Exposure Response
deg_results.parasite <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffExpGene/Results/Parasite_Effect__significant_results.tsv')

## Rows: 3753 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): gene, metadata, value
## dbl (5): log2FoldChange, padj, baseMean, pvalue, stat
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Question 2: Historical Contingency of Parasite Response
deg_results.historical_contingency <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffExpGene/Results/Historical_Contingency__significant_results.tsv')

## Rows: 204 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): gene, metadata, value
## dbl (5): log2FoldChange, padj, baseMean, pvalue, stat
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Question 3: Historical Contingency of Recovery
deg_results.recovery <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffExpGene/Results/Recovery_Analysis__significant_results.tsv')

## Rows: 199 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): gene, metadata, value
## dbl (5): log2FoldChange, padj, baseMean, pvalue, stat
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Expression Data

# Import raw expression data
expr_counts <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Data/Transcriptomics/Results/rnaseq/051525/salmon/salmon.merged.gene_counts_length_scale__Corrected_f136-f138.tsv') %>%
  # Clean up column names
  dplyr::rename_with(~gsub("TS047_RoL_RNA_", "", .), -c(gene_id, gene_name)) %>%
    dplyr::mutate(gene = gene_id, .before = 1) #%>%

## Rows: 39447 Columns: 74
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr  (2): gene_id, gene_name
## dbl (72): TS047_RoL_RNA_110, TS047_RoL_RNA_112, TS047_RoL_RNA_114, TS047_RoL...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

    # dplyr::rename(`136` = `138`)

# Get samples from raw expression data
expr_samples <- colnames(expr_counts)[-c(1:3)] # Exclude gene_id and gene_name columns

Metadata

# Import metadata to get treatment info
metadata <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffExpGene/Results/sample_metadata.tsv') %>%
    dplyr::mutate(Treatment_long = Treatment) %>%
    dplyr::rename(Sample = sample) %>%
  dplyr::mutate(
    Treatment = stringr::str_c(
      dplyr::case_when(Antibiotics == 0 ~ "A-", Antibiotics == 1 ~ "A+"),
      dplyr::case_when(Temperature == 0 ~ " T-", Temperature == 1 ~ " T+"),
      dplyr::case_when(Pathogen == 0 ~ " P-", Pathogen == 1 ~ " P+")
    )
  ) %>%
    dplyr::relocate(Treatment, .after = Pathogen) %>%
    dplyr::arrange(desc(Treatment))

## Rows: 71 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr  (2): Treatment, Treatment_Plot
## dbl (10): sample, Sample_Name, Time, Antibiotics, Temperature, Pathogen, His...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Print summary of available data
cat("\n=== DATA SUMMARY ===\n")

## 
## === DATA SUMMARY ===

# Create summary table
data_summary <- tibble::tibble(
  Metric = c("Total samples in expression data", "Total samples in metadata"),
  Count = c(length(expr_samples), nrow(metadata))
)

# Display summary table
data_summary %>%
  gt::gt() %>%
  gt::tab_header(
    title = "Data Summary",
    subtitle = "Overview of available samples"
  ) %>%
  gt::cols_label(
    Metric = "Data Type",
    Count = "Number of Samples"
  )

Data Summary
Overview of available samples
Data Type	Number of Samples
Total samples in expression data	72
Total samples in metadata	71

# Create treatment distribution table
metadata %>%
  dplyr::count(Treatment) %>%
  dplyr::arrange(desc(n)) %>%
  gt::gt() %>%
  gt::tab_header(
    title = "Treatment Distribution",
    subtitle = "Number of samples per treatment group"
  ) %>%
  gt::cols_label(
    Treatment = "Treatment Group",
    n = "Number of Samples"
  )

Treatment Distribution
Number of samples per treatment group
Treatment Group	Number of Samples
A+ T+ P+	12
A+ T- P+	12
A- T+ P+	12
A- T- P+	11
A+ T+ P-	6
A+ T- P-	6
A- T+ P-	6
A- T- P-	6

Integrated Analysis

Question 0: All

Analysis

## Attempting to load existing results for Question 0...
## Loading results from: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results/results_all_20250630_200742.rds 
## Successfully loaded existing results for Question 0.

## 
## === QUESTION 0 SUMMARY ===

Question 2: Historical Contingency of Parasite Response
Summary of analysis results
Analysis Metric	Result
Question	Historical Contingency of Parasite Response
Base Treatment	A- T- P-
Comparison Treatments	A- T- P+, A+ T- P-, A+ T- P+, A- T+ P-, A- T+ P+, A+ T+ P-, A+ T+ P+
Total Samples	72
Number of Pairwise Comparisons	7

Pairwise Comparison Details
Results for each individual comparison
Comparison	N Samples	N Significant Genes	N Significant Taxa	N Significant Correlations
A- T- P- vs A- T- P+	18	3753	84	20499
A- T- P- vs A+ T- P-	12	35	75	588
A- T- P- vs A+ T- P+	18	6473	93	7888
A- T- P- vs A- T+ P-	12	47	73	3407
A- T- P- vs A- T+ P+	18	6196	89	1969
A- T- P- vs A+ T+ P-	12	117	77	1656
A- T- P- vs A+ T+ P+	18	5017	119	4563

Correlation Results

## 
## Top 10 strongest correlations across all pairwise comparisons:

gene_id	gene_name	TaxaID	correlation	abs_correlation	p_value	fdr	comparison_name	base_treatment	comparison_treatment
LOC103909395	LOC103909395	Vogesella	1.000	1.000	0.00	0.00	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
LOC141376179	LOC141376179	Chitinimonas	1.000	1.000	0.00	0.00	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
LOC141376179	LOC141376179	Flavihumibacter	1.000	1.000	0.00	0.00	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
LOC141376179	LOC141376179	Hyphomicrobium	1.000	1.000	0.00	0.00	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
LOC141376179	LOC141376179	Mesorhizobium	1.000	1.000	0.00	0.00	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
zgc:171500	zgc:171500	Pseudoxanthomonas	1.000	1.000	0.00	0.00	A- T- P- vs A+ T- P+	A- T- P-	A+ T- P+
zgc:171500	zgc:171500	SWB02	1.000	1.000	0.00	0.00	A- T- P- vs A+ T- P+	A- T- P-	A+ T- P+
zgc:171500	zgc:171500	WY65	1.000	1.000	0.00	0.00	A- T- P- vs A+ T- P+	A- T- P-	A+ T- P+
LOC137490512	LOC137490512	Mesorhizobium	1.000	1.000	0.00	0.00	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC141374967	LOC141374967	Afipia	1.000	1.000	0.00	0.00	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-

## Warning: Removed 831498 rows containing non-finite outside the scale range
## (`stat_bin()`).

## 
## Correlation summary by comparison:

comparison_name	n_correlations	mean_correlation	median_correlation	n_significant
A- T- P- vs A+ T+ P+	729756	−0.019	−0.026	4563
A- T- P- vs A+ T+ P-	729756	−0.054	−0.056	1656
A- T- P- vs A+ T- P+	729756	−0.071	−0.096	7888
A- T- P- vs A+ T- P-	729756	−0.002	0.000	588
A- T- P- vs A- T+ P+	729756	0.028	0.033	1969
A- T- P- vs A- T+ P-	729756	−0.096	−0.112	3407
A- T- P- vs A- T- P+	729756	−0.084	−0.079	20499

Partial Correlation Results

## 
## === PARTIAL CORRELATION SUMMARY ===

Partial Correlation Analysis Summary
Question 0: All Treatments - Controlling for worm counts
Analysis Metric	Count
Total partial correlations calculated	700
Significant partial correlations (FDR < 0.1)	554
Top 10 taxa by number of correlations	10
Partial correlations for top 10 taxa	293

## 
## Top 10 taxa by number of significant partial correlations:

Top 10 Taxa by Number of Significant Partial Correlations
Question 0: All Treatments
Taxa ID	Number of Correlations	Mean Absolute Correlation
Culicoidibacter	65	0.527
Tundrisphaera	46	0.576
Polymorphobacter	33	0.659
Rubrivivax	30	0.642
Bosea	21	0.735
Devosia	21	0.648
Gemmobacter	20	0.559
Ensifer	19	0.761
Legionella	19	0.581
Plesiomonas	19	0.350

## 
## Top 10 taxa by number of significant partial correlations:

Top 10 Taxa by Number of Significant Partial Correlations by Comparison
Question 0: All Treatments
Taxa ID	comparison_name	Number of Correlations	Mean Absolute Correlation
Culicoidibacter	A- T- P- vs A- T- P+	47	0.488
Culicoidibacter	A- T- P- vs A- T+ P+	10	0.510
Culicoidibacter	A- T- P- vs A+ T- P-	4	0.846
Culicoidibacter	A- T- P- vs A+ T+ P-	2	0.705
Culicoidibacter	A- T- P- vs A+ T- P+	2	0.726

## 
## Top 10 strongest partial correlations (for top 10 taxa only):

Top 10 Significant Partial Correlations
Question 0: All Treatments - Top 10 taxa only
Gene ID	Taxa ID	Partial Correlation	P-value	FDR	Absolute Correlation	Comparison	base_treatment	comparison_treatment
LOC137490819	Culicoidibacter	0.916	4.90 × 10−2	7.08 × 10−2	0.916	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
gcdhb	Ensifer	0.907	2.50 × 10−2	5.10 × 10−2	0.907	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
gcdhb	Ensifer	0.897	5.19 × 10−2	5.71 × 10−2	0.897	A- T- P- vs A+ T+ P+	A- T- P-	A+ T+ P+
arglu1a	Bosea	−0.883	9.99 × 10−4	1.76 × 10−2	0.883	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
prdx6	Bosea	−0.870	2.00 × 10−3	1.05 × 10−2	0.870	A- T- P- vs A+ T- P+	A- T- P-	A+ T- P+
gvin1l	Polymorphobacter	0.869	9.99 × 10−4	3.84 × 10−3	0.869	A- T- P- vs A+ T+ P+	A- T- P-	A+ T+ P+
abcc2	Culicoidibacter	0.858	2.00 × 10−2	4.76 × 10−2	0.858	A- T- P- vs A- T+ P+	A- T- P-	A- T+ P+
med29	Bosea	−0.857	4.00 × 10−3	6.89 × 10−3	0.857	A- T- P- vs A+ T+ P+	A- T- P-	A+ T+ P+
fn3krp	Ensifer	0.856	1.20 × 10−2	4.00 × 10−2	0.856	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
trpv4	Culicoidibacter	0.855	4.90 × 10−2	7.08 × 10−2	0.855	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-

## 
## Top 10 strongest partial correlations (for top 10 taxa only):

Top 10 Significant Partial Correlations (Culicoidibacter)
Question 0: All Treatments - Top 10 features
Gene ID	Taxa ID	Partial Correlation	P-value	FDR	Absolute Correlation	Comparison	base_treatment	comparison_treatment
LOC137490819	Culicoidibacter	0.916	4.90 × 10−2	7.08 × 10−2	0.916	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
abcc2	Culicoidibacter	0.858	2.00 × 10−2	4.76 × 10−2	0.858	A- T- P- vs A- T+ P+	A- T- P-	A- T+ P+
trpv4	Culicoidibacter	0.855	4.90 × 10−2	7.08 × 10−2	0.855	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
si:ch73-380l3.2	Culicoidibacter	0.850	5.09 × 10−2	7.08 × 10−2	0.850	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
dhrs1	Culicoidibacter	0.795	9.99 × 10−4	7.68 × 10−3	0.795	A- T- P- vs A+ T- P+	A- T- P-	A+ T- P+
bdh1	Culicoidibacter	0.765	5.59 × 10−2	7.46 × 10−2	0.765	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
ano5a	Culicoidibacter	0.765	2.70 × 10−2	5.50 × 10−2	0.765	A- T- P- vs A- T+ P+	A- T- P-	A- T+ P+
mtus1b	Culicoidibacter	0.759	3.10 × 10−2	5.16 × 10−2	0.759	A- T- P- vs A- T- P+	A- T- P-	A- T- P+
ampd2a	Culicoidibacter	0.748	3.90 × 10−2	6.71 × 10−2	0.748	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
si:dkey-286h2.3	Culicoidibacter	0.705	1.80 × 10−2	4.52 × 10−2	0.705	A- T- P- vs A- T- P+	A- T- P-	A- T- P+

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Heatmap

## Warning in matrix(top_correlations$correlation, nrow =
## length(unique(top_correlations$gene_id)), : data length [50] is not a
## sub-multiple or multiple of the number of rows [38]

Question 1: Parasite Exposure Response

Analysis

## Attempting to load existing results for Question 1...
## Loading results from: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results/results_parasite_20250630_200828.rds 
## Successfully loaded existing results for Question 1.

## 
## === QUESTION 1 SUMMARY ===

Question 1: Parasite Exposure Response
Summary of analysis results
Analysis Metric	Result
Question	Parasite Exposure Response
Treatment Comparison	A- T- P- vs A- T- P+
Number of Samples	18
Significant Genes	3753
Significant Taxa	30
Significant Correlations	11681

Correlation Results

## 
## Top 10 strongest correlations:

gene_id	gene_name	TaxaID	correlation	abs_correlation	p_value	fdr
hephl1b	hephl1b	Paucibacter	−0.926	0.926	0.00	0.00
LOC100006250	LOC100006250	Gemmobacter	−0.894	0.894	1.55 × 10−15	6.71 × 10−11
LOC101884777	LOC101884777	Vogesella	0.878	0.878	1.95 × 10−13	5.18 × 10−9
cdk10	cdk10	Culicoidibacter	−0.878	0.878	2.40 × 10−13	5.18 × 10−9
nom1	nom1	Culicoidibacter	−0.876	0.876	4.16 × 10−13	7.19 × 10−9
prpf31	prpf31	Plesiomonas	−0.874	0.874	7.09 × 10−13	1.02 × 10−8
LOC100006250	LOC100006250	Paenirhodobacter	−0.871	0.871	1.21 × 10−12	1.46 × 10−8
homezb	homezb	Plesiomonas	−0.870	0.870	1.53 × 10−12	1.46 × 10−8
srpk1b	srpk1b	Culicoidibacter	−0.870	0.870	1.53 × 10−12	1.46 × 10−8
cdk10	cdk10	Plesiomonas	−0.863	0.863	7.99 × 10−12	6.89 × 10−8

Partial Correlation Results

results.parasite$sig_partial_correlations %>%
  dplyr::filter(fdr < 0.1) %>%
    gt::gt() %>%
  gt::fmt_number(
    columns = c(correlation, p_value, fdr, abs_correlation),
    decimals = 4
  )

gene_id	taxa_id	correlation	p_value	fdr	abs_correlation
spsb1	Chitinilyticum	0.8373	0.0010	0.0333	0.8373
mtus1b	Culicoidibacter	0.7594	0.0370	0.0528	0.7594
si:dkey-286h2.3	Culicoidibacter	0.7045	0.0090	0.0500	0.7045
znf692	Gemmobacter	−0.6923	0.0290	0.0507	0.6923
rfc3	Paucibacter	−0.6875	0.0040	0.0444	0.6875
bbc3	Culicoidibacter	0.6846	0.0160	0.0500	0.6846
gng10	Paucibacter	−0.6839	0.0010	0.0333	0.6839
pdk2a	Paucibacter	0.6745	0.0220	0.0500	0.6745
uqcc6	Paucibacter	−0.6266	0.0040	0.0444	0.6266
slc25a44a	Culicoidibacter	−0.5864	0.0210	0.0500	0.5864
nrbp2b	Culicoidibacter	0.5854	0.0699	0.0874	0.5854
sco2	Tundrisphaera	−0.5594	0.0260	0.0500	0.5594
idh2	Culicoidibacter	−0.5593	0.0140	0.0500	0.5593
mtrex	Culicoidibacter	−0.5557	0.0240	0.0500	0.5557
ing5b	Culicoidibacter	−0.5511	0.0210	0.0500	0.5511
prpf31	Culicoidibacter	−0.5418	0.0150	0.0500	0.5418
gtdc1	Culicoidibacter	−0.5403	0.0430	0.0581	0.5403
LOC100006250	Gemmobacter	−0.5378	0.0060	0.0500	0.5378
aaas	Culicoidibacter	−0.5357	0.0390	0.0541	0.5357
uchl5	Culicoidibacter	−0.5234	0.0260	0.0500	0.5234
hephl1b	Paucibacter	−0.5218	0.0040	0.0444	0.5218
uqcc6	Culicoidibacter	−0.5206	0.0340	0.0507	0.5206
cdk10	Culicoidibacter	−0.5191	0.0250	0.0500	0.5191
ncbp1	Culicoidibacter	−0.5160	0.0230	0.0500	0.5160
recql4	Paucibacter	−0.5148	0.0040	0.0444	0.5148
ciao1	Culicoidibacter	−0.5084	0.0250	0.0500	0.5084
commd9	Culicoidibacter	−0.5082	0.0130	0.0500	0.5082
zgc:77262	Culicoidibacter	−0.5081	0.0320	0.0507	0.5081
srpk1b	Culicoidibacter	−0.5045	0.0200	0.0500	0.5045
rmi1	Culicoidibacter	−0.5036	0.0470	0.0618	0.5036
haus6	Culicoidibacter	−0.4997	0.0100	0.0500	0.4997
prps1b	Culicoidibacter	−0.4993	0.0330	0.0507	0.4993
psmc4	Culicoidibacter	−0.4912	0.0280	0.0507	0.4912
nsfl1c	Culicoidibacter	−0.4905	0.0140	0.0500	0.4905
si:ch211-165i18.2	Paucibacter	−0.4893	0.0010	0.0333	0.4893
tardbpb	Culicoidibacter	−0.4868	0.0170	0.0500	0.4868
rbm12bb	Culicoidibacter	−0.4792	0.0240	0.0500	0.4792
denr	Culicoidibacter	−0.4761	0.0360	0.0528	0.4761
dmap1	Culicoidibacter	−0.4684	0.0200	0.0500	0.4684
psmd4a	Culicoidibacter	−0.4671	0.0130	0.0500	0.4671
pigf	Culicoidibacter	−0.4670	0.0330	0.0507	0.4670
psmd6	Culicoidibacter	−0.4670	0.0140	0.0500	0.4670
fus	Culicoidibacter	−0.4665	0.0200	0.0500	0.4665
psmd1	Culicoidibacter	−0.4656	0.0230	0.0500	0.4656
psmd2	Culicoidibacter	−0.4632	0.0150	0.0500	0.4632
ssbp1	Culicoidibacter	−0.4609	0.0559	0.0717	0.4609
igbp1	Culicoidibacter	−0.4607	0.0110	0.0500	0.4607
gng10	Paenirhodobacter	−0.4589	0.0020	0.0400	0.4589
ppp1r11	Culicoidibacter	−0.4575	0.0180	0.0500	0.4575
tubb4b	Culicoidibacter	−0.4499	0.0280	0.0507	0.4499
lonp1	Culicoidibacter	−0.4499	0.0140	0.0500	0.4499
gfpt1	Culicoidibacter	−0.4486	0.0400	0.0547	0.4486
tuba8l4	Culicoidibacter	−0.4482	0.0170	0.0500	0.4482
psmd3	Culicoidibacter	−0.4411	0.0300	0.0507	0.4411
imp4	Culicoidibacter	−0.4375	0.0260	0.0500	0.4375
pfkpa	Culicoidibacter	−0.4332	0.0390	0.0541	0.4332
ctbp1l	Culicoidibacter	−0.4319	0.0260	0.0500	0.4319
nom1	Culicoidibacter	−0.4299	0.0210	0.0500	0.4299
h2ax	Culicoidibacter	−0.4279	0.0460	0.0613	0.4279
mmp25b	Culicoidibacter	−0.4273	0.0310	0.0507	0.4273
mcts1	Culicoidibacter	−0.4271	0.0500	0.0649	0.4271
csf3r	Culicoidibacter	−0.4197	0.0180	0.0500	0.4197
zgc:85858	Culicoidibacter	−0.4182	0.0330	0.0507	0.4182
h3f3a	Culicoidibacter	−0.4170	0.0060	0.0500	0.4170
mrpl33	Culicoidibacter	−0.4102	0.0250	0.0500	0.4102
arf2b	Culicoidibacter	−0.4096	0.0300	0.0507	0.4096
tars1	Culicoidibacter	−0.4086	0.0340	0.0507	0.4086
si:ch211-284o19.8	Culicoidibacter	−0.4082	0.0370	0.0528	0.4082
LOC100006250	Paenirhodobacter	−0.4073	0.0020	0.0400	0.4073
tubb2b	Culicoidibacter	−0.3988	0.0180	0.0500	0.3988
hephl1b	Paenirhodobacter	−0.3883	0.0150	0.0500	0.3883
anxa5b	Culicoidibacter	−0.3796	0.0130	0.0500	0.3796
armc7	Paenirhodobacter	−0.3574	0.0080	0.0500	0.3574
agr2	Culicoidibacter	−0.3543	0.0340	0.0507	0.3543
msraa	Polymorphobacter	−0.3441	0.0160	0.0500	0.3441
LOC101884777	Vogesella	0.3276	0.0619	0.0784	0.3276
p2rx4b	Gemmobacter	−0.3085	0.0310	0.0507	0.3085
zgc:172339	Gemmobacter	−0.2649	0.0200	0.0500	0.2649
LOC137487483	Paenirhodobacter	−0.2322	0.0210	0.0500	0.2322
pvalb7	Gemmobacter	−0.2287	0.0290	0.0507	0.2287

# Count of significant partial correlations
results.parasite$sig_partial_correlations %>%
  dplyr::filter(fdr < 0.1) %>%
  dplyr::summarise(
    n_significant = n()
  ) %>%
  gt::gt() %>%
  gt::tab_header(
    title = "Number of Significant Partial Correlations",
    subtitle = "Parasite Exposure Response (FDR < 0.1)"
  ) %>%
  gt::cols_label(
    n_significant = "Number of Significant Correlations"
  )

Number of Significant Partial Correlations
Parasite Exposure Response (FDR < 0.1)
Number of Significant Correlations
80

# Count of significant partial correlations by direction
results.parasite$sig_partial_correlations %>%
  dplyr::filter(fdr < 0.1) %>%
  dplyr::mutate(
    direction = dplyr::case_when(
      correlation > 0 ~ "Positive",
      correlation < 0 ~ "Negative",
      TRUE ~ "Zero"
    )
  ) %>%
  dplyr::group_by(direction) %>%
  dplyr::summarise(
    n_correlations = n(),
    .groups = "drop"
  ) %>%
  gt::gt() %>%
  gt::tab_header(
    title = "Significant Partial Correlations by Direction",
    subtitle = "Parasite Exposure Response (FDR < 0.1)"
  ) %>%
  gt::cols_label(
    direction = "Correlation Direction",
    n_correlations = "Number of Correlations"
  )

Significant Partial Correlations by Direction
Parasite Exposure Response (FDR < 0.1)
Correlation Direction	Number of Correlations
Negative	73
Positive	7

## 
## === PARTIAL CORRELATION SUMMARY ===
## 
## Top 10 strongest partial correlations:

Heatmap

## Warning in matrix(top_correlations$correlation, nrow =
## length(unique(top_correlations$gene_id)), : data length [100] is not a
## sub-multiple or multiple of the number of rows [87]

Question 2: Historical Contingency of Parasite Response

Analysis

## Attempting to load existing results for Question 2...
## Loading results from: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results/results_historical_contingency_20250630_200918.rds 
## Successfully loaded existing results for Question 2.

## 
## === QUESTION 2 SUMMARY ===

Question 2: Historical Contingency of Parasite Response
Summary of analysis results
Analysis Metric	Result
Question	Historical Contingency of Parasite Response
Base Treatment	A- T- P+
Comparison Treatments	A+ T- P+, A- T+ P+, A+ T+ P+
Total Samples	48
Number of Pairwise Comparisons	3

Pairwise Comparison Details
Results for each individual comparison
Comparison	N Samples	N Significant Genes	N Significant Taxa	N Significant Correlations
A- T- P+ vs A+ T- P+	24	49	48	1
A- T- P+ vs A- T+ P+	24	137	16	3
A- T- P+ vs A+ T+ P+	24	18	33	0

Correlation Results

## 
## Top 10 strongest correlations across all pairwise comparisons:

gene_id	gene_name	TaxaID	correlation	abs_correlation	p_value	fdr	comparison_name	base_treatment	comparison_treatment
zmp:0000000629	zmp:0000000629	Polymorphobacter	−0.716	0.716	1.54 × 10−6	5.33 × 10−3	A- T- P+ vs A- T+ P+	A- T- P+	A- T+ P+
zmp:0000000629	zmp:0000000629	Chryseotalea	−0.683	0.683	1.16 × 10−5	1.37 × 10−2	A- T- P+ vs A- T+ P+	A- T- P+	A- T+ P+
lsm14b	lsm14b	Polymorphobacter	−0.683	0.683	1.19 × 10−5	1.37 × 10−2	A- T- P+ vs A- T+ P+	A- T- P+	A- T+ P+
zranb2	zranb2	Uliginosibacterium	0.682	0.682	1.22 × 10−5	4.39 × 10−2	A- T- P+ vs A+ T- P+	A- T- P+	A+ T- P+

## Warning: Removed 994 rows containing non-finite outside the scale range
## (`stat_bin()`).

## 
## Correlation summary by comparison:

comparison_name	n_correlations	mean_correlation	median_correlation	n_significant
A- T- P+ vs A+ T+ P+	4004	−0.059	−0.069	0
A- T- P+ vs A+ T- P+	4004	−0.071	−0.080	1
A- T- P+ vs A- T+ P+	4004	−0.033	−0.038	3

Partial Correlation Results

results.historical_contingency[["combined_sig_partial_correlations"]] %>%
  dplyr::filter(fdr < 0.1) %>%
    gt::gt() %>%
  gt::fmt_number(
    columns = c(correlation, p_value, fdr, abs_correlation),
    decimals = 4
  )

gene_id	taxa_id	correlation	p_value	fdr	abs_correlation	comparison_name	base_treatment	comparison_treatment
zranb2	Uliginosibacterium	0.6274	0.0240	0.0240	0.6274	A- T- P+ vs A+ T- P+	A- T- P+	A+ T- P+
lsm14b	Polymorphobacter	−0.3765	0.0100	0.0300	0.3765	A- T- P+ vs A- T+ P+	A- T- P+	A- T+ P+

# Count of significant partial correlations
results.historical_contingency[["combined_sig_partial_correlations"]] %>%
  dplyr::filter(fdr < 0.1) %>%
    dplyr::group_by(comparison_name) %>%
  dplyr::summarise(
    n_significant = n()
  ) %>%
  gt::gt() %>%
  gt::tab_header(
    title = "Number of Significant Partial Correlations",
    subtitle = "Historical Contingency of Parasite Response (FDR < 0.1)"
  ) %>%
  gt::cols_label(
    n_significant = "Number of Significant Correlations"
  )

Number of Significant Partial Correlations
Historical Contingency of Parasite Response (FDR < 0.1)
comparison_name	Number of Significant Correlations
A- T- P+ vs A+ T- P+	1
A- T- P+ vs A- T+ P+	1

# Count of significant partial correlations by direction
results.historical_contingency[["combined_sig_partial_correlations"]] %>%
  dplyr::filter(fdr < 0.1) %>%
  dplyr::mutate(
    direction = dplyr::case_when(
      correlation > 0 ~ "Positive",
      correlation < 0 ~ "Negative",
      TRUE ~ "Zero"
    )
  ) %>%
  dplyr::group_by(comparison_name, direction) %>%
  dplyr::summarise(
    n_correlations = n()#,
    # .groups = "drop"
  ) %>%
  gt::gt() %>%
  gt::tab_header(
    title = "Significant Partial Correlations by Direction",
    subtitle = "Historical Contingency of Parasite Response (FDR < 0.1)"
  ) %>%
  gt::cols_label(
    direction = "Correlation Direction",
    n_correlations = "Number of Correlations"
  )

## `summarise()` has grouped output by 'comparison_name'. You can override using
## the `.groups` argument.

Significant Partial Correlations by Direction
Historical Contingency of Parasite Response (FDR < 0.1)
Correlation Direction	Number of Correlations
A- T- P+ vs A+ T- P+
Positive	1
A- T- P+ vs A- T+ P+
Negative	1

## 
## === PARTIAL CORRELATION SUMMARY ===
## 
## Top 10 strongest partial correlations:

Heatmap

## Warning in matrix(top_correlations$correlation, nrow =
## length(unique(top_correlations$gene_id)), : data length [4] is not a
## sub-multiple or multiple of the number of rows [3]

Question 3: Historical Contingency of Recovery

Analysis

## Attempting to load existing results for Question 3...
## Loading results from: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results/results_recovery_20250630_201002.rds 
## Successfully loaded existing results for Question 3.

## 
## === QUESTION 3 SUMMARY ===

Question 3: Historical Contingency of Recovery
Summary of analysis results
Analysis Metric	Result
Question	Historical Contingency of Recovery
Base Treatment	A- T- P-
Comparison Treatments	A+ T- P-, A- T+ P-, A+ T+ P-
Total Samples	24
Number of Pairwise Comparisons	3

Pairwise Comparison Details
Results for each individual comparison
Comparison	N Samples	N Significant Genes	N Significant Taxa	N Significant Correlations
A- T- P- vs A+ T- P-	12	35	59	16
A- T- P- vs A- T+ P-	12	47	65	45
A- T- P- vs A+ T+ P-	12	117	72	83

Correlation Results

## 
## Top 10 strongest correlations across all pairwise comparisons:

gene_id	gene_name	TaxaID	correlation	abs_correlation	p_value	fdr	comparison_name	base_treatment	comparison_treatment
LOC795984	LOC795984	Luteolibacter	1.000	1.000	0.00	0.00	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC795984	LOC795984	Piscinibacter	1.000	1.000	0.00	0.00	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
blnk	blnk	Reyranella	−0.940	0.940	0.00	0.00	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
pmaip1	pmaip1	Elstera	−0.921	0.921	7.99 × 10−14	1.78 × 10−10	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
elmo2	elmo2	Aquihabitans	−0.892	0.892	4.48 × 10−10	7.50 × 10−7	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC101885452	LOC101885452	Dinghuibacter	0.889	0.889	8.39 × 10−10	5.77 × 10−6	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
LOC137496053	LOC137496053	Gemmobacter	−0.887	0.887	1.16 × 10−9	3.49 × 10−6	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC141375138	LOC141375138	Runella	0.886	0.886	1.48 × 10−9	3.49 × 10−6	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
zmp:0000000527	zmp:0000000527	Reyranella	−0.883	0.883	2.85 × 10−9	5.04 × 10−6	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC137487668	LOC137487668	Aquihabitans	0.871	0.871	1.93 × 10−8	2.59 × 10−5	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-

## Warning: Removed 558 rows containing non-finite outside the scale range
## (`stat_bin()`).

## 
## Correlation summary by comparison:

comparison_name	n_correlations	mean_correlation	median_correlation	n_significant
A- T- P- vs A+ T+ P-	7068	−0.009	−0.007	83
A- T- P- vs A+ T- P-	7068	0.030	0.021	16
A- T- P- vs A- T+ P-	7068	−0.074	−0.092	45

Partial Correlation Results

# Display all significant partial correlations
results.recovery[["combined_sig_partial_correlations"]] %>%
  dplyr::filter(fdr < 0.1) %>%
  gt::gt() %>%
  gt::fmt_number(
    columns = c(correlation, p_value, fdr, abs_correlation),
    decimals = 4
  )

gene_id	taxa_id	correlation	p_value	fdr	abs_correlation	comparison_name	base_treatment	comparison_treatment
adam12b	Flavobacterium	0.4367	0.0040	0.0320	0.4367	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
pla2g3	Flavobacterium	0.4298	0.0040	0.0320	0.4298	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
LOC141379342	Aquihabitans	0.2077	0.0170	0.0906	0.2077	A- T- P- vs A+ T- P-	A- T- P-	A+ T- P-
LOC795984	Luteolibacter	1.0000	0.0010	0.0169	1.0000	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC795984	Piscinibacter	1.0000	0.0030	0.0169	1.0000	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC137489722	Pseudoxanthomonas	0.9788	0.0400	0.0620	0.9788	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC141379342	Pseudoxanthomonas	0.9769	0.0270	0.0593	0.9769	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC141379345	Pseudoxanthomonas	0.9545	0.0080	0.0327	0.9545	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
lhx8a	Rhodococcus	0.9070	0.0360	0.0620	0.9070	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC101882847	Pseudoxanthomonas	0.9014	0.0020	0.0169	0.9014	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC141379353	Pseudoxanthomonas	0.8066	0.0030	0.0169	0.8066	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
maml3	Bosea	−0.7730	0.0110	0.0380	0.7730	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
pmaip1	Crenobacter	0.7504	0.0509	0.0674	0.7504	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC137487211	Flavobacterium	−0.7311	0.0390	0.0620	0.7311	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
maml3	Pirellula	−0.7069	0.0020	0.0169	0.7069	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
mcfd2	Bosea	0.6966	0.0130	0.0417	0.6966	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC110439307	Pseudoxanthomonas	−0.6611	0.0819	0.0970	0.6611	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC560278	Sphaerotilus	−0.6609	0.0260	0.0593	0.6609	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
znf1069	Caulobacter	0.6585	0.0180	0.0539	0.6585	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
spire1a	Pirellula	−0.6172	0.0030	0.0169	0.6172	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC141379337	Pseudoxanthomonas	0.6016	0.0030	0.0169	0.6016	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
ftr34	Sphaerotilus	−0.5770	0.0490	0.0668	0.5770	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
maml3	Reyranella	−0.5753	0.0010	0.0169	0.5753	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
pmaip1	Pseudoxanthomonas	−0.5609	0.0290	0.0593	0.5609	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
pmaip1	Agitococcus	−0.5603	0.0380	0.0620	0.5603	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC101882702	Roseomonas	−0.5545	0.0579	0.0737	0.5545	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
si:ch211-217k17.11	Runella	−0.5458	0.0260	0.0593	0.5458	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC110438438	Pseudoxanthomonas	0.5388	0.0040	0.0200	0.5388	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
si:ch211-217k17.11	Sphaerotilus	−0.5309	0.0330	0.0611	0.5309	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC100001589	Runella	−0.5034	0.0080	0.0327	0.5034	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
dhx58	Reyranella	−0.5010	0.0110	0.0380	0.5010	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
dhx58	Runella	−0.4941	0.0220	0.0582	0.4941	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
pmaip1	Bdellovibrio	−0.4541	0.0420	0.0629	0.4541	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
si:ch211-15j1.4	Reyranella	−0.4488	0.0290	0.0593	0.4488	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
si:ch211-15j1.4	Runella	−0.4367	0.0310	0.0606	0.4367	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC101883008	Culicoidibacter	−0.4241	0.0470	0.0668	0.4241	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
pmaip1	Elstera	−0.3968	0.0480	0.0668	0.3968	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
LOC137487211	Culicoidibacter	−0.3781	0.0589	0.0737	0.3781	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
elmo2	Aquihabitans	−0.3665	0.0340	0.0611	0.3665	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
si:ch211-15j1.4	Agitococcus	−0.3400	0.0709	0.0863	0.3400	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
slco1c1	Aquihabitans	0.2119	0.0210	0.0582	0.2119	A- T- P- vs A- T+ P-	A- T- P-	A- T+ P-
rergla	Pseudorhodoplanes	0.9493	0.0500	0.0846	0.9493	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
znf1069	Legionella	0.8945	0.0390	0.0703	0.8945	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC141375138	Runella	0.8897	0.0060	0.0415	0.8897	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
prag1	Pirellula	−0.8807	0.0170	0.0522	0.8807	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
zmp:0000000527	Pirellula	−0.8723	0.0070	0.0434	0.8723	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
cxcl14	Bosea	−0.8514	0.0060	0.0415	0.8514	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
spire1a	Pirellula	−0.8270	0.0060	0.0415	0.8270	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
blnk	Pirellula	−0.7939	0.0100	0.0434	0.7939	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC100537840	Pirellula	−0.7683	0.0050	0.0415	0.7683	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
tnfa	Pirellula	−0.7665	0.0090	0.0434	0.7665	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
faah	Pirellula	−0.7363	0.0160	0.0510	0.7363	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
ly6m5	Flavobacterium	0.7361	0.0160	0.0510	0.7361	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
vps33a	Bosea	−0.7246	0.0040	0.0415	0.7246	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
rnf213a	Pirellula	−0.7227	0.0020	0.0415	0.7227	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
irf3	Flavobacterium	0.7109	0.0330	0.0684	0.7109	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC101883092	Pirellula	−0.7093	0.0250	0.0576	0.7093	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC103910189	Pirellula	−0.6807	0.0240	0.0576	0.6807	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
sash1b	Pirellula	−0.6599	0.0240	0.0576	0.6599	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
vim	Bosea	−0.6590	0.0110	0.0434	0.6590	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
pstpip1b	Pirellula	−0.6541	0.0230	0.0576	0.6541	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
hspa8	Gemmobacter	−0.6432	0.0430	0.0743	0.6432	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
tmem106a	Bosea	−0.6313	0.0030	0.0415	0.6313	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC141385619	Culicoidibacter	0.6274	0.0080	0.0434	0.6274	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
zgc:56106	Runella	−0.5926	0.0010	0.0415	0.5926	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC137496184	Flavobacterium	−0.5884	0.0120	0.0452	0.5884	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
cxcl14	Flavobacterium	−0.5850	0.0160	0.0510	0.5850	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
si:ch1073-406l10.2	Neochlamydia	0.5778	0.0030	0.0415	0.5778	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
si:ch1073-406l10.2	Gemmobacter	−0.5757	0.0330	0.0684	0.5757	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
prr5a	Flavobacterium	−0.5724	0.0380	0.0703	0.5724	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC560278	Luteolibacter	−0.5508	0.0210	0.0576	0.5508	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC141378197	Runella	−0.5370	0.0370	0.0703	0.5370	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC795984	Pirellula	0.5311	0.0300	0.0672	0.5311	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC137496184	Bosea	−0.5222	0.0040	0.0415	0.5222	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC101883092	Runella	−0.5080	0.0220	0.0576	0.5080	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC793010	Dongia	−0.5044	0.0360	0.0703	0.5044	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC137496184	Gemmobacter	−0.4932	0.0060	0.0415	0.4932	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC100537840	Runella	−0.4756	0.0559	0.0892	0.4756	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC103910189	Runella	−0.4714	0.0250	0.0576	0.4714	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC137496053	Gemmobacter	−0.4630	0.0110	0.0434	0.4630	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
si:ch211-125e6.14	Tundrisphaera	−0.4618	0.0320	0.0684	0.4618	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
prr5a	Bosea	−0.4563	0.0190	0.0563	0.4563	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
igf2bp2b	Runella	−0.4547	0.0210	0.0576	0.4547	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
ugt5g1	Bosea	−0.4413	0.0110	0.0434	0.4413	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
ugt5g1	Flavobacterium	−0.3948	0.0410	0.0723	0.3948	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC101882702	Neochlamydia	−0.3904	0.0639	0.0962	0.3904	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC108183350	Luteolibacter	−0.3691	0.0350	0.0703	0.3691	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
blnk	Reyranella	−0.3672	0.0390	0.0703	0.3672	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
ugt5g1	Neochlamydia	0.3628	0.0030	0.0415	0.3628	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC100537840	Reyranella	−0.3400	0.0569	0.0892	0.3400	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
zgc:171506	Crenobacter	0.3351	0.0090	0.0434	0.3351	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
zmp:0000000527	Reyranella	−0.3274	0.0130	0.0469	0.3274	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
rnf213a	Reyranella	−0.3250	0.0649	0.0962	0.3250	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
pld4	Reyranella	−0.2990	0.0529	0.0879	0.2990	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
prag1	Reyranella	−0.2889	0.0080	0.0434	0.2889	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
LOC141375335	Reyranella	−0.2362	0.0549	0.0892	0.2362	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-
igf2bp2b	Reyranella	−0.2110	0.0589	0.0906	0.2110	A- T- P- vs A+ T+ P-	A- T- P-	A+ T+ P-

# Count of significant partial correlations
results.recovery[["combined_sig_partial_correlations"]] %>%
  dplyr::filter(fdr < 0.1) %>%
    dplyr::group_by(comparison_name) %>%
  dplyr::summarise(
    n_significant = n()
  ) %>%
  gt::gt() %>%
  gt::tab_header(
    title = "Number of Significant Partial Correlations",
    subtitle = "Historical Contingency of Recovery (FDR < 0.1)"
  ) %>%
  gt::cols_label(
    n_significant = "Number of Significant Correlations"
  )

Number of Significant Partial Correlations
Historical Contingency of Recovery (FDR < 0.1)
comparison_name	Number of Significant Correlations
A- T- P- vs A+ T+ P-	56
A- T- P- vs A+ T- P-	3
A- T- P- vs A- T+ P-	38

# Count of significant partial correlations by direction
results.recovery[["combined_sig_partial_correlations"]] %>%
  dplyr::filter(fdr < 0.1) %>%
  dplyr::mutate(
    direction = dplyr::case_when(
      correlation > 0 ~ "Positive",
      correlation < 0 ~ "Negative",
      TRUE ~ "Zero"
    )
  ) %>%
  dplyr::group_by(comparison_name,direction) %>%
  dplyr::summarise(
    n_correlations = n()#,
    # .groups = "drop"
  ) %>%
  gt::gt() %>%
  gt::tab_header(
    title = "Significant Partial Correlations by Direction",
    subtitle = "Historical Contingency of Recovery (FDR < 0.1)"
  ) %>%
  gt::cols_label(
    direction = "Correlation Direction",
    n_correlations = "Number of Correlations"
  )

## `summarise()` has grouped output by 'comparison_name'. You can override using
## the `.groups` argument.

Significant Partial Correlations by Direction
Historical Contingency of Recovery (FDR < 0.1)
Correlation Direction	Number of Correlations
A- T- P- vs A+ T+ P-
Negative	46
Positive	10
A- T- P- vs A+ T- P-
Positive	3
A- T- P- vs A- T+ P-
Negative	24
Positive	14

## 
## === PARTIAL CORRELATION SUMMARY ===
## 
## Top 10 strongest partial correlations:

Heatmap

## [1] 50

## Warning in matrix(top_correlations$correlation, nrow =
## length(unique(top_correlations$gene_id)), : data length [144] is not a
## sub-multiple or multiple of the number of rows [78]

Comparative Analysis

Summary Statistics Across Questions

Summary Comparison Across Research Questions
Integrated DEGxDAT Analysis Results
Research Question	Treatment Comparison	N Samples	N_Pairwise_Comparisons	N Significant Genes	N Significant Taxa	N Significant Correlations	N Significant Partial Correlations	Mean Correlation	Median Correlation
Parasite Exposure Response	A- T- P- vs A- T- P+	18	1	3753	30	11681	80	−0.100	−0.088
Historical Contingency of Parasite Response	A- T- P+ vs (A+ T- P+, A- T+ P+, A+ T+ P+)	48	3	612	123	4	2	−0.055	−0.063
Historical Contingency of Recovery	A- T- P- vs (A+ T- P-, A- T+ P-, A+ T+ P-)	24	3	597	219	144	97	−0.017	−0.022

Overlap Analysis

## 
## === GENE OVERLAP STATISTICS ===

Gene Overlap Statistics
Distribution of significant genes across research questions
Overlap Category	Number of Genes
Total unique genes across all questions	3948
Genes in Parasite Response only	3582
Genes in Historical Contingency only	87
Genes in Recovery only	108
Genes in Parasite Response and Historical Contingency	95
Genes in Parasite Response and Recovery	78
Genes in Historical Contingency and Recovery	2
Genes in all three	2

## 
## === TAXA OVERLAP STATISTICS ===

Taxa Overlap Statistics
Distribution of significant taxa across research questions
Overlap Category	Number of Taxa
Total unique taxa across all questions	95
Taxa in Parasite Response only	12
Taxa in Historical Contingency only	19
Taxa in Recovery only	37
Taxa in Parasite Response and Historical Contingency	9
Taxa in Parasite Response and Recovery	15
Taxa in Historical Contingency and Recovery	15
Taxa in all three	6

Enrichment Analysis

Question 0: All Treatments

## 'select()' returned 1:many mapping between keys and columns

## Warning in dplyr::left_join(., entrez_map, by = "gene_id"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 2 of `x` matches multiple rows in `y`.
## ℹ Row 7 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

## 
## Processing contrast with 467 genes

## 'select()' returned 1:1 mapping between keys and columns

## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## 
## === GO ENRICHMENT - ALL TREATMENTS ===

Top 10 Enriched GO Terms
All Treatments
GO ID	GO Term Description	Adjusted P-value	Mean Correlation	N Positive	N Negative
GO:0006997	nucleus organization	8.21 × 10−3	−0.191	2	5
GO:0019941	modification-dependent protein catabolic process	1.37 × 10−2	0.046	11	11
GO:0043632	modification-dependent macromolecule catabolic process	1.37 × 10−2	0.046	11	11
GO:0006511	ubiquitin-dependent protein catabolic process	1.71 × 10−2	0.069	11	10
GO:0006998	nuclear envelope organization	9.69 × 10−2	−0.239	1	3

Question 1: Parasite Exposure Response

## 'select()' returned 1:many mapping between keys and columns

## Warning in dplyr::left_join(., entrez_map, by = "gene_id"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 2 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

## 
## Processing contrast with 2272 genes

## 'select()' returned 1:1 mapping between keys and columns

## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## 
## === GO ENRICHMENT - PARASITE EXPOSURE RESPONSE ===

Top 10 Enriched GO Terms
Parasite Exposure Response
GO ID	GO Term Description	Adjusted P-value	Mean Correlation	N Positive	N Negative
GO:0006260	DNA replication	1.46 × 10−42	−0.719	1	82
GO:0007059	chromosome segregation	7.90 × 10−38	−0.717	1	88
GO:0006974	DNA damage response	6.83 × 10−35	−0.563	16	124
GO:1903047	mitotic cell cycle process	3.76 × 10−33	−0.644	7	107
GO:0006281	DNA repair	6.48 × 10−32	−0.601	10	105
GO:0051276	chromosome organization	8.58 × 10−32	−0.700	2	95
GO:0006261	DNA-templated DNA replication	3.17 × 10−31	−0.741	0	55
GO:0098813	nuclear chromosome segregation	2.08 × 10−27	−0.734	0	59
GO:0000819	sister chromatid segregation	2.66 × 10−27	−0.733	0	50
GO:0048285	organelle fission	4.52 × 10−27	−0.658	4	74

Question 2: Historical Contingency of Parasite Response

## 'select()' returned 1:1 mapping between keys and columns

## Warning in dplyr::left_join(., entrez_map, by = "gene_id"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 2 of `x` matches multiple rows in `y`.
## ℹ Row 2 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

## 
## Processing contrast with 3 genes

## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## 
## === GO ENRICHMENT - HISTORICAL CONTINGENCY OF PARASITE RESPONSE ===

Top 10 Enriched GO Terms
Historical Contingency of Parasite Response
GO ID	GO Term Description	Adjusted P-value	Mean Correlation	N Positive	N Negative
GO:0006952	defense response	5.18 × 10−2	0.682	1	0
GO:0009607	response to biotic stimulus	5.18 × 10−2	0.682	1	0
GO:0009617	response to bacterium	5.18 × 10−2	0.682	1	0
GO:0042742	defense response to bacterium	5.18 × 10−2	0.682	1	0
GO:0043207	response to external biotic stimulus	5.18 × 10−2	0.682	1	0
GO:0051707	response to other organism	5.18 × 10−2	0.682	1	0
GO:0098542	defense response to other organism	5.18 × 10−2	0.682	1	0

Question 3: Historical Contingency of Recovery

## 'select()' returned 1:1 mapping between keys and columns

## Warning in dplyr::left_join(., entrez_map, by = "gene_id"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

## 
## Processing contrast with 60 genes

## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## 
## === GO ENRICHMENT - HISTORICAL CONTINGENCY OF RECOVERY ===

Top 10 Enriched GO Terms
Historical Contingency of Recovery
GO ID	GO Term Description	Adjusted P-value	Mean Correlation	N Positive	N Negative
GO:0006952	defense response	3.02 × 10−2	−0.527	1	5
GO:0045109	intermediate filament organization	8.60 × 10−2	0.043	1	1

Comprehensive Enrichment Analysis and Results Export

Setup and Functions

# Function to create comprehensive enrichment tables for significant correlations
create_comprehensive_enrichment_tables <- function(results_list, analysis_name) {
  # This function creates comprehensive tables of significant gene-taxa correlations
  # with GO and KEGG annotations for a given analysis
  
  cat("\n=== Creating comprehensive enrichment tables for:", analysis_name, "===\n")
  
  # Get significant partial correlations
  if ("sig_partial_correlations" %in% names(results_list)) {
    sig_correlations <- results_list$sig_partial_correlations
  } else if ("combined_sig_partial_correlations" %in% names(results_list)) {
    sig_correlations <- results_list$combined_sig_partial_correlations
  } else {
    cat("No significant partial correlations found for", analysis_name, "\n")
    return(NULL)
  }
  
  if (nrow(sig_correlations) == 0) {
    cat("No significant partial correlations found for", analysis_name, "\n")
    return(NULL)
  }
  
  # Get unique genes from significant correlations
  unique_genes <- unique(sig_correlations$gene_id)
  cat("Number of unique genes:", length(unique_genes), "\n")
  
  # Map genes to Entrez IDs and get annotations
  # First, get Entrez IDs
  entrez_ids <- AnnotationDbi::mapIds(
    org.Dr.eg.db,
    keys = unique_genes,
    column = "ENTREZID",
    keytype = "SYMBOL",
    multiVals = "first"
  )
  
  # Then, get gene names
  gene_names <- AnnotationDbi::mapIds(
    org.Dr.eg.db,
    keys = unique_genes,
    column = "GENENAME",
    keytype = "SYMBOL",
    multiVals = "first"
  )
  
  # Create annotation dataframe
  gene_annotations <- tibble::tibble(
    gene_id = unique_genes,
    entrez_id = as.character(entrez_ids),
    gene_name = gene_names,
    symbol = unique_genes  # Since we're using SYMBOL as keytype, symbol is the same as gene_id
  ) %>%
    dplyr::filter(!is.na(entrez_id))
  
  # Perform GO enrichment for these genes
  if (nrow(gene_annotations) > 0) {
    go_enrichment <- enrichGO(
      gene = gene_annotations$entrez_id,
      OrgDb = org.Dr.eg.db,
      keyType = "ENTREZID",
      ont = "BP",
      pAdjustMethod = "BH",
      pvalueCutoff = 0.1,
      qvalueCutoff = 0.2,
      readable = TRUE
    )
    
    if (!is.null(go_enrichment) && nrow(go_enrichment) > 0) {
      go_results <- as.data.frame(go_enrichment) %>%
        dplyr::select(ID, Description, p.adjust, Count, geneID) %>%
        dplyr::mutate(log_padj = -log10(p.adjust))
    } else {
      go_results <- data.frame()
    }
    
    # Perform KEGG enrichment for these genes
    kegg_enrichment <- enrichKEGG(
      gene = gene_annotations$entrez_id,
      organism = 'dre',
      keyType = 'ncbi-geneid',
      pAdjustMethod = "BH",
      pvalueCutoff = 0.1,
      qvalueCutoff = 0.2
    )
    
    if (!is.null(kegg_enrichment) && nrow(kegg_enrichment) > 0) {
      kegg_results <- as.data.frame(kegg_enrichment) %>%
        dplyr::select(ID, Description, p.adjust, Count, geneID) %>%
        dplyr::mutate(log_padj = -log10(p.adjust))
    } else {
      kegg_results <- data.frame()
    }
  } else {
    go_results <- data.frame()
    kegg_results <- data.frame()
  }
  
  # Create comprehensive correlation table with annotations
  comprehensive_table <- sig_correlations %>%
    dplyr::left_join(gene_annotations, by = "gene_id") %>%
    dplyr::mutate(
      gene_description = ifelse(!is.na(gene_name), gene_name, gene_id),
      analysis = analysis_name
    ) %>%
    dplyr::select(
      analysis, gene_id, gene_description, taxa_id, correlation, 
      p_value, fdr, abs_correlation, entrez_id
    ) %>%
    dplyr::arrange(desc(abs_correlation))
  
  # Add GO term annotations to the comprehensive table
  if (nrow(go_results) > 0) {
    go_gene_mapping <- go_results %>%
      tidyr::separate_rows(geneID, sep = "/") %>%
      dplyr::select(ID, Description, geneID) %>%
      dplyr::rename(go_id = ID, go_description = Description, gene_symbol = geneID)
    
    comprehensive_table <- comprehensive_table %>%
      dplyr::left_join(go_gene_mapping, by = c("gene_id" = "gene_symbol")) %>%
      dplyr::group_by(analysis, gene_id, gene_description, taxa_id, correlation, 
                      p_value, fdr, abs_correlation, entrez_id) %>%
      dplyr::summarise(
        go_terms = paste(unique(go_description), collapse = "; "),
        .groups = "drop"
      )
  } else {
    comprehensive_table <- comprehensive_table %>%
      dplyr::mutate(go_terms = NA_character_)
  }
  
  # Add KEGG pathway annotations to the comprehensive table
  if (nrow(kegg_results) > 0) {
    kegg_gene_mapping <- kegg_results %>%
      tidyr::separate_rows(geneID, sep = "/") %>%
      dplyr::select(ID, Description, geneID) %>%
      dplyr::rename(kegg_id = ID, kegg_description = Description, gene_symbol = geneID)
    
    comprehensive_table <- comprehensive_table %>%
      dplyr::left_join(kegg_gene_mapping, by = c("gene_id" = "gene_symbol")) %>%
      dplyr::group_by(analysis, gene_id, gene_description, taxa_id, correlation, 
                      p_value, fdr, abs_correlation, entrez_id, go_terms) %>%
      dplyr::summarise(
        kegg_pathways = paste(unique(kegg_description), collapse = "; "),
        .groups = "drop"
      )
  } else {
    comprehensive_table <- comprehensive_table %>%
      dplyr::mutate(kegg_pathways = NA_character_)
  }
  
  return(list(
    comprehensive_table = comprehensive_table,
    go_results = go_results,
    kegg_results = kegg_results,
    gene_annotations = gene_annotations
  ))
}

# Function to export results for each analysis
export_analysis_results <- function(results_list, analysis_name, results_dir = RESULTS_DIR) {
  # This function exports comprehensive results for a given analysis
  
  cat("\n=== Exporting results for:", analysis_name, "===\n")
  
  # Create timestamp
  timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S")
  
  # Create analysis-specific directory
  analysis_dir <- file.path(results_dir, analysis_name)
  if (!dir.exists(analysis_dir)) {
    dir.create(analysis_dir, recursive = TRUE)
  }
  
  # Export significant correlations
  if ("sig_correlations" %in% names(results_list) && nrow(results_list$sig_correlations) > 0) {
    correlations_file <- file.path(analysis_dir, paste0("significant_correlations_", timestamp, ".tsv"))
    readr::write_tsv(results_list$sig_correlations, correlations_file)
    cat("Exported significant correlations to:", correlations_file, "\n")
  }
  
  # Export significant partial correlations
  if ("sig_partial_correlations" %in% names(results_list) && nrow(results_list$sig_partial_correlations) > 0) {
    partial_correlations_file <- file.path(analysis_dir, paste0("significant_partial_correlations_", timestamp, ".tsv"))
    readr::write_tsv(results_list$sig_partial_correlations, partial_correlations_file)
    cat("Exported significant partial correlations to:", partial_correlations_file, "\n")
  } else if ("combined_sig_partial_correlations" %in% names(results_list) && nrow(results_list$combined_sig_partial_correlations) > 0) {
    partial_correlations_file <- file.path(analysis_dir, paste0("significant_partial_correlations_", timestamp, ".tsv"))
    readr::write_tsv(results_list$combined_sig_partial_correlations, partial_correlations_file)
    cat("Exported significant partial correlations to:", partial_correlations_file, "\n")
  }
  
  # Export all correlations
  if ("correlation_results" %in% names(results_list) && nrow(results_list$correlation_results) > 0) {
    all_correlations_file <- file.path(analysis_dir, paste0("all_correlations_", timestamp, ".tsv"))
    readr::write_tsv(results_list$correlation_results, all_correlations_file)
    cat("Exported all correlations to:", all_correlations_file, "\n")
  } else if ("combined_all_correlations" %in% names(results_list) && nrow(results_list$combined_all_correlations) > 0) {
    all_correlations_file <- file.path(analysis_dir, paste0("all_correlations_", timestamp, ".tsv"))
    readr::write_tsv(results_list$combined_all_correlations, all_correlations_file)
    cat("Exported all correlations to:", all_correlations_file, "\n")
  }
  
  # Export DEG results
  if ("deg_sig" %in% names(results_list) && nrow(results_list$deg_sig) > 0) {
    deg_file <- file.path(analysis_dir, paste0("significant_genes_", timestamp, ".tsv"))
    readr::write_tsv(results_list$deg_sig, deg_file)
    cat("Exported significant genes to:", deg_file, "\n")
  } else if ("combined_deg" %in% names(results_list) && nrow(results_list$combined_deg) > 0) {
    deg_file <- file.path(analysis_dir, paste0("significant_genes_", timestamp, ".tsv"))
    readr::write_tsv(results_list$combined_deg, deg_file)
    cat("Exported significant genes to:", deg_file, "\n")
  }
  
  # Export DAT results
  if ("dat_sig" %in% names(results_list) && nrow(results_list$dat_sig) > 0) {
    dat_file <- file.path(analysis_dir, paste0("significant_taxa_", timestamp, ".tsv"))
    readr::write_tsv(results_list$dat_sig, dat_file)
    cat("Exported significant taxa to:", dat_file, "\n")
  } else if ("combined_dat" %in% names(results_list) && nrow(results_list$combined_dat) > 0) {
    dat_file <- file.path(analysis_dir, paste0("significant_taxa_", timestamp, ".tsv"))
    readr::write_tsv(results_list$combined_dat, dat_file)
    cat("Exported significant taxa to:", dat_file, "\n")
  }
  
  # Create and export comprehensive enrichment tables
  enrichment_results <- create_comprehensive_enrichment_tables(results_list, analysis_name)
  
  if (!is.null(enrichment_results)) {
    # Export comprehensive table
    comprehensive_file <- file.path(analysis_dir, paste0("comprehensive_gene_taxa_correlations_", timestamp, ".tsv"))
    readr::write_tsv(enrichment_results$comprehensive_table, comprehensive_file)
    cat("Exported comprehensive gene-taxa correlations to:", comprehensive_file, "\n")
    
    # Export GO results
    if (nrow(enrichment_results$go_results) > 0) {
      go_file <- file.path(analysis_dir, paste0("go_enrichment_", timestamp, ".tsv"))
      readr::write_tsv(enrichment_results$go_results, go_file)
      cat("Exported GO enrichment results to:", go_file, "\n")
    }
    
    # Export KEGG results
    if (nrow(enrichment_results$kegg_results) > 0) {
      kegg_file <- file.path(analysis_dir, paste0("kegg_enrichment_", timestamp, ".tsv"))
      readr::write_tsv(enrichment_results$kegg_results, kegg_file)
      cat("Exported KEGG enrichment results to:", kegg_file, "\n")
    }
    
    # Export gene annotations
    if (nrow(enrichment_results$gene_annotations) > 0) {
      annotations_file <- file.path(analysis_dir, paste0("gene_annotations_", timestamp, ".tsv"))
      readr::write_tsv(enrichment_results$gene_annotations, annotations_file)
      cat("Exported gene annotations to:", annotations_file, "\n")
    }
  }
  
  cat("Export complete for:", analysis_name, "\n")
  return(enrichment_results)
}

Question 0: All Treatments Analysis

## 
## === COMPREHENSIVE ENRICHMENT ANALYSIS - QUESTION 0: ALL TREATMENTS ===

## 
## === Creating comprehensive enrichment tables for: All_Treatments ===
## Number of unique genes: 501

## 'select()' returned 1:many mapping between keys and columns

## 'select()' returned 1:1 mapping between keys and columns

## Reading KEGG annotation online: "https://rest.kegg.jp/link/dre/pathway"...

## Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/dre"...

## Reading KEGG annotation online: "https://rest.kegg.jp/conv/ncbi-geneid/dre"...

## Warning in dplyr::left_join(., go_gene_mapping, by = c(gene_id = "gene_symbol")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 22 of `x` matches multiple rows in `y`.
## ℹ Row 17 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

## $comprehensive_table
## <div id="eaqgdmjilt" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
##   <style>#eaqgdmjilt table {
##   font-family: system-ui, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol', 'Noto Color Emoji';
##   -webkit-font-smoothing: antialiased;
##   -moz-osx-font-smoothing: grayscale;
## }
## 
## #eaqgdmjilt thead, #eaqgdmjilt tbody, #eaqgdmjilt tfoot, #eaqgdmjilt tr, #eaqgdmjilt td, #eaqgdmjilt th {
##   border-style: none;
## }
## 
## #eaqgdmjilt p {
##   margin: 0;
##   padding: 0;
## }
## 
## #eaqgdmjilt .gt_table {
##   display: table;
##   border-collapse: collapse;
##   line-height: normal;
##   margin-left: auto;
##   margin-right: auto;
##   color: #333333;
##   font-size: 16px;
##   font-weight: normal;
##   font-style: normal;
##   background-color: #FFFFFF;
##   width: auto;
##   border-top-style: solid;
##   border-top-width: 2px;
##   border-top-color: #A8A8A8;
##   border-right-style: none;
##   border-right-width: 2px;
##   border-right-color: #D3D3D3;
##   border-bottom-style: solid;
##   border-bottom-width: 2px;
##   border-bottom-color: #A8A8A8;
##   border-left-style: none;
##   border-left-width: 2px;
##   border-left-color: #D3D3D3;
## }
## 
## #eaqgdmjilt .gt_caption {
##   padding-top: 4px;
##   padding-bottom: 4px;
## }
## 
## #eaqgdmjilt .gt_title {
##   color: #333333;
##   font-size: 125%;
##   font-weight: initial;
##   padding-top: 4px;
##   padding-bottom: 4px;
##   padding-left: 5px;
##   padding-right: 5px;
##   border-bottom-color: #FFFFFF;
##   border-bottom-width: 0;
## }
## 
## #eaqgdmjilt .gt_subtitle {
##   color: #333333;
##   font-size: 85%;
##   font-weight: initial;
##   padding-top: 3px;
##   padding-bottom: 5px;
##   padding-left: 5px;
##   padding-right: 5px;
##   border-top-color: #FFFFFF;
##   border-top-width: 0;
## }
## 
## #eaqgdmjilt .gt_heading {
##   background-color: #FFFFFF;
##   text-align: center;
##   border-bottom-color: #FFFFFF;
##   border-left-style: none;
##   border-left-width: 1px;
##   border-left-color: #D3D3D3;
##   border-right-style: none;
##   border-right-width: 1px;
##   border-right-color: #D3D3D3;
## }
## 
## #eaqgdmjilt .gt_bottom_border {
##   border-bottom-style: solid;
##   border-bottom-width: 2px;
##   border-bottom-color: #D3D3D3;
## }
## 
## #eaqgdmjilt .gt_col_headings {
##   border-top-style: solid;
##   border-top-width: 2px;
##   border-top-color: #D3D3D3;
##   border-bottom-style: solid;
##   border-bottom-width: 2px;
##   border-bottom-color: #D3D3D3;
##   border-left-style: none;
##   border-left-width: 1px;
##   border-left-color: #D3D3D3;
##   border-right-style: none;
##   border-right-width: 1px;
##   border-right-color: #D3D3D3;
## }
## 
## #eaqgdmjilt .gt_col_heading {
##   color: #333333;
##   background-color: #FFFFFF;
##   font-size: 100%;
##   font-weight: normal;
##   text-transform: inherit;
##   border-left-style: none;
##   border-left-width: 1px;
##   border-left-color: #D3D3D3;
##   border-right-style: none;
##   border-right-width: 1px;
##   border-right-color: #D3D3D3;
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##   <thead>
##     <tr class="gt_heading">
##       <td colspan="11" class="gt_heading gt_title gt_font_normal" style>Top 20 Significant Gene-Taxa Correlations</td>
##     </tr>
##     <tr class="gt_heading">
##       <td colspan="11" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>All Treatments Analysis</td>
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##     <tr class="gt_col_headings">
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Analysis">Analysis</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene ID">Gene ID</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene Description">Gene Description</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Taxa ID">Taxa ID</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Correlation">Correlation</th>
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##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="GO Terms">GO Terms</th>
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##   <tbody class="gt_table_body">
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100006250</td>
## <td headers="gene_description" class="gt_row gt_left">annexin A2-A-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Gemmobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.538</td>
## <td headers="p_value" class="gt_row gt_right">1.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.33 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.538</td>
## <td headers="entrez_id" class="gt_row gt_right">100006250</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100006250</td>
## <td headers="gene_description" class="gt_row gt_left">annexin A2-A-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Paenirhodobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.407</td>
## <td headers="p_value" class="gt_row gt_right">3.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">2.31 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.407</td>
## <td headers="entrez_id" class="gt_row gt_right">100006250</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100148399</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC100148399</td>
## <td headers="taxa_id" class="gt_row gt_left">Ensifer</td>
## <td headers="correlation" class="gt_row gt_right">0.620</td>
## <td headers="p_value" class="gt_row gt_right">6.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">3.89 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.620</td>
## <td headers="entrez_id" class="gt_row gt_right">100148399</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100149761</td>
## <td headers="gene_description" class="gt_row gt_left">dispanin subfamily A member 2b-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Crenobacter</td>
## <td headers="correlation" class="gt_row gt_right">0.672</td>
## <td headers="p_value" class="gt_row gt_right">4.60 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.672</td>
## <td headers="entrez_id" class="gt_row gt_right">100149761</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100149991</td>
## <td headers="gene_description" class="gt_row gt_left">E3 ubiquitin-protein ligase TRIM39-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Tundrisphaera</td>
## <td headers="correlation" class="gt_row gt_right">−0.606</td>
## <td headers="p_value" class="gt_row gt_right">3.60 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">6.10 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.606</td>
## <td headers="entrez_id" class="gt_row gt_right">100149991</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100150003</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC100150003</td>
## <td headers="taxa_id" class="gt_row gt_left">Polymorphobacter</td>
## <td headers="correlation" class="gt_row gt_right">0.709</td>
## <td headers="p_value" class="gt_row gt_right">1.40 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">1.84 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.709</td>
## <td headers="entrez_id" class="gt_row gt_right">100150003</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100150882</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC100150882</td>
## <td headers="taxa_id" class="gt_row gt_left">Polymorphobacter</td>
## <td headers="correlation" class="gt_row gt_right">0.798</td>
## <td headers="p_value" class="gt_row gt_right">7.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">1.14 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.798</td>
## <td headers="entrez_id" class="gt_row gt_right">100150882</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100330485</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC100330485</td>
## <td headers="taxa_id" class="gt_row gt_left">Tundrisphaera</td>
## <td headers="correlation" class="gt_row gt_right">−0.546</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.08 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.546</td>
## <td headers="entrez_id" class="gt_row gt_right">100330485</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100331609</td>
## <td headers="gene_description" class="gt_row gt_left">GTPase IMAP family member 8-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Gemmobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.671</td>
## <td headers="p_value" class="gt_row gt_right">1.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.671</td>
## <td headers="entrez_id" class="gt_row gt_right">100331609</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100332293</td>
## <td headers="gene_description" class="gt_row gt_left">WD repeat domain phosphoinositide-interacting protein 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Gemmobacter</td>
## <td headers="correlation" class="gt_row gt_right">0.523</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">2.22 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.523</td>
## <td headers="entrez_id" class="gt_row gt_right">100332293</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100334101</td>
## <td headers="gene_description" class="gt_row gt_left">protein NLRC3-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Devosia</td>
## <td headers="correlation" class="gt_row gt_right">0.801</td>
## <td headers="p_value" class="gt_row gt_right">5.09 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">8.09 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.801</td>
## <td headers="entrez_id" class="gt_row gt_right">100334101</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882521</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC101882521</td>
## <td headers="taxa_id" class="gt_row gt_left">Aquihabitans</td>
## <td headers="correlation" class="gt_row gt_right">0.813</td>
## <td headers="p_value" class="gt_row gt_right">2.70 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.813</td>
## <td headers="entrez_id" class="gt_row gt_right">101882521</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882521</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC101882521</td>
## <td headers="taxa_id" class="gt_row gt_left">Bradyrhizobium</td>
## <td headers="correlation" class="gt_row gt_right">0.805</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">2.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.805</td>
## <td headers="entrez_id" class="gt_row gt_right">101882521</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882521</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC101882521</td>
## <td headers="taxa_id" class="gt_row gt_left">Mycobacterium</td>
## <td headers="correlation" class="gt_row gt_right">0.889</td>
## <td headers="p_value" class="gt_row gt_right">2.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.78 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.889</td>
## <td headers="entrez_id" class="gt_row gt_right">101882521</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882521</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC101882521</td>
## <td headers="taxa_id" class="gt_row gt_left">Nocardia</td>
## <td headers="correlation" class="gt_row gt_right">0.737</td>
## <td headers="p_value" class="gt_row gt_right">5.39 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.29 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.737</td>
## <td headers="entrez_id" class="gt_row gt_right">101882521</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882702</td>
## <td headers="gene_description" class="gt_row gt_left">golgin subfamily A member 6-like protein 22</td>
## <td headers="taxa_id" class="gt_row gt_left">Hyphomicrobium</td>
## <td headers="correlation" class="gt_row gt_right">−0.359</td>
## <td headers="p_value" class="gt_row gt_right">6.19 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.40 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.359</td>
## <td headers="entrez_id" class="gt_row gt_right">101882702</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882702</td>
## <td headers="gene_description" class="gt_row gt_left">golgin subfamily A member 6-like protein 22</td>
## <td headers="taxa_id" class="gt_row gt_left">Mycobacterium</td>
## <td headers="correlation" class="gt_row gt_right">−0.444</td>
## <td headers="p_value" class="gt_row gt_right">3.10 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">8.85 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.444</td>
## <td headers="entrez_id" class="gt_row gt_right">101882702</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882702</td>
## <td headers="gene_description" class="gt_row gt_left">golgin subfamily A member 6-like protein 22</td>
## <td headers="taxa_id" class="gt_row gt_left">Paucibacter</td>
## <td headers="correlation" class="gt_row gt_right">0.825</td>
## <td headers="p_value" class="gt_row gt_right">6.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">3.04 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.825</td>
## <td headers="entrez_id" class="gt_row gt_right">101882702</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101883939</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC101883939</td>
## <td headers="taxa_id" class="gt_row gt_left">Vibrio</td>
## <td headers="correlation" class="gt_row gt_right">0.504</td>
## <td headers="p_value" class="gt_row gt_right">9.99 × 10<sup style='font-size: 65%;'>−4</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.68 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.504</td>
## <td headers="entrez_id" class="gt_row gt_right">101883939</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101884777</td>
## <td headers="gene_description" class="gt_row gt_left">butyrophilin-like protein 2</td>
## <td headers="taxa_id" class="gt_row gt_left">Vogesella</td>
## <td headers="correlation" class="gt_row gt_right">0.328</td>
## <td headers="p_value" class="gt_row gt_right">6.29 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">8.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.328</td>
## <td headers="entrez_id" class="gt_row gt_right">101884777</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##   </tbody>
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##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene IDs">Gene IDs</th>
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##     </tr>
##   </thead>
##   <tbody class="gt_table_body">
##     <tr><td headers="ID" class="gt_row gt_left">GO:0006997</td>
## <td headers="Description" class="gt_row gt_left">nucleus organization</td>
## <td headers="p.adjust" class="gt_row gt_right">8.21 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="Count" class="gt_row gt_right">7</td>
## <td headers="geneID" class="gt_row gt_left">wrap53/chmp2a/bend3/nsfl1c/bmb/coil/lmnl3</td>
## <td headers="log_padj" class="gt_row gt_right">2.09</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0019941</td>
## <td headers="Description" class="gt_row gt_left">modification-dependent protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">1.37 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">22</td>
## <td headers="geneID" class="gt_row gt_left">uchl5/psmd4a/psmd6/lonp1/erlec1/ufl1/ube2h/rnf111/prkn/ube2d2/ube2d2l/nsfl1c/psmd11b/psmc3/uba7/uba1/herc56.2/psma6a/usp11/psmc1a/psmc2/usp38</td>
## <td headers="log_padj" class="gt_row gt_right">1.86</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0043632</td>
## <td headers="Description" class="gt_row gt_left">modification-dependent macromolecule catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">1.37 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">22</td>
## <td headers="geneID" class="gt_row gt_left">uchl5/psmd4a/psmd6/lonp1/erlec1/ufl1/ube2h/rnf111/prkn/ube2d2/ube2d2l/nsfl1c/psmd11b/psmc3/uba7/uba1/herc56.2/psma6a/usp11/psmc1a/psmc2/usp38</td>
## <td headers="log_padj" class="gt_row gt_right">1.86</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0006511</td>
## <td headers="Description" class="gt_row gt_left">ubiquitin-dependent protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">1.71 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">21</td>
## <td headers="geneID" class="gt_row gt_left">uchl5/psmd4a/psmd6/erlec1/ufl1/ube2h/rnf111/prkn/ube2d2/ube2d2l/nsfl1c/psmd11b/psmc3/uba7/uba1/herc56.2/psma6a/usp11/psmc1a/psmc2/usp38</td>
## <td headers="log_padj" class="gt_row gt_right">1.77</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0006998</td>
## <td headers="Description" class="gt_row gt_left">nuclear envelope organization</td>
## <td headers="p.adjust" class="gt_row gt_right">9.69 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
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## <td headers="log_padj" class="gt_row gt_right">1.01</td></tr>
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##       <td colspan="6" class="gt_heading gt_title gt_font_normal" style>KEGG Pathway Enrichment Results</td>
##     </tr>
##     <tr class="gt_heading">
##       <td colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>All Treatments Analysis</td>
##     </tr>
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##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="KEGG ID">KEGG ID</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Pathway Description">Pathway Description</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Adjusted P-value">Adjusted P-value</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Gene Count">Gene Count</th>
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##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="-log10(adj. P-value)">-log10(adj. P-value)</th>
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##   <tbody class="gt_table_body">
##     <tr><td headers="ID" class="gt_row gt_left">dre03050</td>
## <td headers="Description" class="gt_row gt_left">Proteasome</td>
## <td headers="p.adjust" class="gt_row gt_right">1.10 × 10<sup style='font-size: 65%;'>−4</sup></td>
## <td headers="Count" class="gt_row gt_right">8</td>
## <td headers="geneID" class="gt_row gt_right">415202/393261/393518/322265/321947/171585/336786/393941</td>
## <td headers="log_padj" class="gt_row gt_right">3.96</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre04210</td>
## <td headers="Description" class="gt_row gt_left">Apoptosis</td>
## <td headers="p.adjust" class="gt_row gt_right">2.47 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">10</td>
## <td headers="geneID" class="gt_row gt_right">393154/561021/751765/567460/794666/195817/563053/552927/791531/58022</td>
## <td headers="log_padj" class="gt_row gt_right">1.61</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre04621</td>
## <td headers="Description" class="gt_row gt_left">NOD-like receptor signaling pathway</td>
## <td headers="p.adjust" class="gt_row gt_right">2.47 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">9</td>
## <td headers="geneID" class="gt_row gt_right">492489/393620/561021/100149641/100150844/567460/794666/100334992/58022</td>
## <td headers="log_padj" class="gt_row gt_right">1.61</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre04981</td>
## <td headers="Description" class="gt_row gt_left">Folate transport and metabolism</td>
## <td headers="p.adjust" class="gt_row gt_right">2.47 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_right">323301/393561/100073336/404609</td>
## <td headers="log_padj" class="gt_row gt_right">1.61</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre04625</td>
## <td headers="Description" class="gt_row gt_left">C-type lectin receptor signaling pathway</td>
## <td headers="p.adjust" class="gt_row gt_right">5.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">7</td>
## <td headers="geneID" class="gt_row gt_right">192322/561021/100150844/565241/794666/246227/58022</td>
## <td headers="log_padj" class="gt_row gt_right">1.30</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre04146</td>
## <td headers="Description" class="gt_row gt_left">Peroxisome</td>
## <td headers="p.adjust" class="gt_row gt_right">5.49 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">6</td>
## <td headers="geneID" class="gt_row gt_right">566378/573990/566863/100034578/100037349/334644</td>
## <td headers="log_padj" class="gt_row gt_right">1.26</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre03410</td>
## <td headers="Description" class="gt_row gt_left">Base excision repair</td>
## <td headers="p.adjust" class="gt_row gt_right">7.87 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_right">259256/550335/393949/571485</td>
## <td headers="log_padj" class="gt_row gt_right">1.10</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre00140</td>
## <td headers="Description" class="gt_row gt_left">Steroid hormone biosynthesis</td>
## <td headers="p.adjust" class="gt_row gt_right">8.25 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">5</td>
## <td headers="geneID" class="gt_row gt_right">324340/100379286/100415797/678597/334098</td>
## <td headers="log_padj" class="gt_row gt_right">1.08</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre04120</td>
## <td headers="Description" class="gt_row gt_left">Ubiquitin mediated proteolysis</td>
## <td headers="p.adjust" class="gt_row gt_right">8.25 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">7</td>
## <td headers="geneID" class="gt_row gt_right">368425/550328/393934/335444/100001302/406335/565383</td>
## <td headers="log_padj" class="gt_row gt_right">1.08</td></tr>
##   </tbody>
##   
##   
## </table>
## </div>
## 
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## </style>
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##   <thead>
##     <tr class="gt_heading">
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##     </tr>
##     <tr class="gt_heading">
##       <td colspan="2" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>All Treatments Analysis</td>
##     </tr>
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##     </tr>
##   </thead>
##   <tbody class="gt_table_body">
##     <tr><td headers="Metric" class="gt_row gt_left">Total significant gene-taxa correlations</td>
## <td headers="Value" class="gt_row gt_right">554</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Unique genes involved</td>
## <td headers="Value" class="gt_row gt_right">501</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Unique taxa involved</td>
## <td headers="Value" class="gt_row gt_right">63</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Enriched GO terms</td>
## <td headers="Value" class="gt_row gt_right">5</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Enriched KEGG pathways</td>
## <td headers="Value" class="gt_row gt_right">9</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Mean absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.606</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Median absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.609</td></tr>
##   </tbody>
##   
##   
## </table>
## </div>

## 
## Top 20 significant gene-taxa correlations with annotations:
## 
## Top 15 enriched GO terms:
## 
## Top 15 enriched KEGG pathways:

Top 15 Enriched KEGG Pathways
All Treatments Analysis
KEGG ID	KEGG Pathway Description	Adjusted P-value	Gene Count	Gene IDs	-log10(adj p-value)
dre03050	Proteasome	1.10 × 10−4	8	415202/393261/393518/322265/321947/171585/336786/393941	3.957
dre04210	Apoptosis	2.47 × 10−2	10	393154/561021/751765/567460/794666/195817/563053/552927/791531/58022	1.608
dre04621	NOD-like receptor signaling pathway	2.47 × 10−2	9	492489/393620/561021/100149641/100150844/567460/794666/100334992/58022	1.608
dre04981	Folate transport and metabolism	2.47 × 10−2	4	323301/393561/100073336/404609	1.608
dre04625	C-type lectin receptor signaling pathway	5.07 × 10−2	7	192322/561021/100150844/565241/794666/246227/58022	1.295
dre04146	Peroxisome	5.49 × 10−2	6	566378/573990/566863/100034578/100037349/334644	1.260
dre03410	Base excision repair	7.87 × 10−2	4	259256/550335/393949/571485	1.104
dre00140	Steroid hormone biosynthesis	8.25 × 10−2	5	324340/100379286/100415797/678597/334098	1.084
dre04120	Ubiquitin mediated proteolysis	8.25 × 10−2	7	368425/550328/393934/335444/100001302/406335/565383	1.084

Question 1: Parasite Exposure Response

## 
## === COMPREHENSIVE ENRICHMENT ANALYSIS - QUESTION 1: PARASITE EXPOSURE RESPONSE ===

## 
## === Creating comprehensive enrichment tables for: Parasite_Exposure_Response ===
## Number of unique genes: 76

## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## Warning in dplyr::left_join(., go_gene_mapping, by = c(gene_id = "gene_symbol")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 59 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

## $comprehensive_table
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##   <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
##   <thead>
##     <tr class="gt_heading">
##       <td colspan="11" class="gt_heading gt_title gt_font_normal" style>Top 20 Significant Gene-Taxa Correlations</td>
##     </tr>
##     <tr class="gt_heading">
##       <td colspan="11" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Parasite Exposure Response</td>
##     </tr>
##     <tr class="gt_col_headings">
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Analysis">Analysis</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene ID">Gene ID</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene Description">Gene Description</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Taxa ID">Taxa ID</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Correlation">Correlation</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="P-value">P-value</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="FDR">FDR</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Abs Correlation">Abs Correlation</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Entrez ID">Entrez ID</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="GO Terms">GO Terms</th>
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##   <tbody class="gt_table_body">
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100006250</td>
## <td headers="gene_description" class="gt_row gt_left">annexin A2-A-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Gemmobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.538</td>
## <td headers="p_value" class="gt_row gt_right">5.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.538</td>
## <td headers="entrez_id" class="gt_row gt_right">100006250</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100006250</td>
## <td headers="gene_description" class="gt_row gt_left">annexin A2-A-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Paenirhodobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.407</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.407</td>
## <td headers="entrez_id" class="gt_row gt_right">100006250</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101884777</td>
## <td headers="gene_description" class="gt_row gt_left">butyrophilin-like protein 2</td>
## <td headers="taxa_id" class="gt_row gt_left">Vogesella</td>
## <td headers="correlation" class="gt_row gt_right">0.328</td>
## <td headers="p_value" class="gt_row gt_right">6.19 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.84 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.328</td>
## <td headers="entrez_id" class="gt_row gt_right">101884777</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137487483</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137487483</td>
## <td headers="taxa_id" class="gt_row gt_left">Paenirhodobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.232</td>
## <td headers="p_value" class="gt_row gt_right">2.10 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.232</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">aaas</td>
## <td headers="gene_description" class="gt_row gt_left">achalasia, adrenocortical insufficiency, alacrimia</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.536</td>
## <td headers="p_value" class="gt_row gt_right">3.90 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.41 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.536</td>
## <td headers="entrez_id" class="gt_row gt_right">378454</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">agr2</td>
## <td headers="gene_description" class="gt_row gt_left">anterior gradient 2</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.354</td>
## <td headers="p_value" class="gt_row gt_right">3.40 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.354</td>
## <td headers="entrez_id" class="gt_row gt_right">335616</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">anxa5b</td>
## <td headers="gene_description" class="gt_row gt_left">annexin A5b</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.380</td>
## <td headers="p_value" class="gt_row gt_right">1.30 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.380</td>
## <td headers="entrez_id" class="gt_row gt_right">337132</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">arf2b</td>
## <td headers="gene_description" class="gt_row gt_left">ADP-ribosylation factor 2b</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.410</td>
## <td headers="p_value" class="gt_row gt_right">3.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.410</td>
## <td headers="entrez_id" class="gt_row gt_right">327026</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">armc7</td>
## <td headers="gene_description" class="gt_row gt_left">armadillo repeat containing 7</td>
## <td headers="taxa_id" class="gt_row gt_left">Paenirhodobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.357</td>
## <td headers="p_value" class="gt_row gt_right">7.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.357</td>
## <td headers="entrez_id" class="gt_row gt_right">767659</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">bbc3</td>
## <td headers="gene_description" class="gt_row gt_left">BCL2 binding component 3</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">0.685</td>
## <td headers="p_value" class="gt_row gt_right">1.60 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.685</td>
## <td headers="entrez_id" class="gt_row gt_right">751763</td>
## <td headers="go_terms" class="gt_row gt_left">mitochondrion organization</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">cdk10</td>
## <td headers="gene_description" class="gt_row gt_left">cyclin dependent kinase 10</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.519</td>
## <td headers="p_value" class="gt_row gt_right">2.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.519</td>
## <td headers="entrez_id" class="gt_row gt_right">550285</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">ciao1</td>
## <td headers="gene_description" class="gt_row gt_left">cytosolic iron-sulfur assembly component 1</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.508</td>
## <td headers="p_value" class="gt_row gt_right">2.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.508</td>
## <td headers="entrez_id" class="gt_row gt_right">795104</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">commd9</td>
## <td headers="gene_description" class="gt_row gt_left">COMM domain containing 9</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.508</td>
## <td headers="p_value" class="gt_row gt_right">1.30 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.508</td>
## <td headers="entrez_id" class="gt_row gt_right">562660</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">csf3r</td>
## <td headers="gene_description" class="gt_row gt_left">colony stimulating factor 3 receptor</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.420</td>
## <td headers="p_value" class="gt_row gt_right">1.80 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.420</td>
## <td headers="entrez_id" class="gt_row gt_right">100134935</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">ctbp1l</td>
## <td headers="gene_description" class="gt_row gt_left">ctbp1l</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.432</td>
## <td headers="p_value" class="gt_row gt_right">2.60 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.432</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">denr</td>
## <td headers="gene_description" class="gt_row gt_left">density-regulated protein</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.476</td>
## <td headers="p_value" class="gt_row gt_right">3.60 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.28 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.476</td>
## <td headers="entrez_id" class="gt_row gt_right">436970</td>
## <td headers="go_terms" class="gt_row gt_left">ribonucleoprotein complex biogenesis; protein-RNA complex assembly; protein-RNA complex organization</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">dmap1</td>
## <td headers="gene_description" class="gt_row gt_left">DNA methyltransferase 1 associated protein 1</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.468</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.468</td>
## <td headers="entrez_id" class="gt_row gt_right">393225</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">fus</td>
## <td headers="gene_description" class="gt_row gt_left">FUS RNA binding protein</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.466</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.466</td>
## <td headers="entrez_id" class="gt_row gt_right">394058</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">gfpt1</td>
## <td headers="gene_description" class="gt_row gt_left">glutamine--fructose-6-phosphate transaminase 1</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.449</td>
## <td headers="p_value" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.47 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.449</td>
## <td headers="entrez_id" class="gt_row gt_right">567861</td>
## <td headers="go_terms" class="gt_row gt_left">fructose 6-phosphate metabolic process</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">gng10</td>
## <td headers="gene_description" class="gt_row gt_left">guanine nucleotide binding protein (G protein), gamma 10</td>
## <td headers="taxa_id" class="gt_row gt_left">Paenirhodobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.459</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.459</td>
## <td headers="entrez_id" class="gt_row gt_right">796780</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
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##       <td colspan="6" class="gt_heading gt_title gt_font_normal" style>Gene Ontology Enrichment Results</td>
##     </tr>
##     <tr class="gt_heading">
##       <td colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Parasite Exposure Response</td>
##     </tr>
##     <tr class="gt_col_headings">
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="GO ID">GO ID</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="GO Term Description">GO Term Description</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Adjusted P-value">Adjusted P-value</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Gene Count">Gene Count</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene IDs">Gene IDs</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="-log10(adj. P-value)">-log10(adj. P-value)</th>
##     </tr>
##   </thead>
##   <tbody class="gt_table_body">
##     <tr><td headers="ID" class="gt_row gt_left">GO:0019941</td>
## <td headers="Description" class="gt_row gt_left">modification-dependent protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">8.75 × 10<sup style='font-size: 65%;'>−4</sup></td>
## <td headers="Count" class="gt_row gt_right">10</td>
## <td headers="geneID" class="gt_row gt_left">spsb1/uchl5/psmc4/nsfl1c/psmd4a/psmd6/psmd1/ppp1r11/lonp1/psmd3</td>
## <td headers="log_padj" class="gt_row gt_right">3.058</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0043632</td>
## <td headers="Description" class="gt_row gt_left">modification-dependent macromolecule catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">8.75 × 10<sup style='font-size: 65%;'>−4</sup></td>
## <td headers="Count" class="gt_row gt_right">10</td>
## <td headers="geneID" class="gt_row gt_left">spsb1/uchl5/psmc4/nsfl1c/psmd4a/psmd6/psmd1/ppp1r11/lonp1/psmd3</td>
## <td headers="log_padj" class="gt_row gt_right">3.058</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0006511</td>
## <td headers="Description" class="gt_row gt_left">ubiquitin-dependent protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">3.08 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="Count" class="gt_row gt_right">9</td>
## <td headers="geneID" class="gt_row gt_left">spsb1/uchl5/psmc4/nsfl1c/psmd4a/psmd6/psmd1/ppp1r11/psmd3</td>
## <td headers="log_padj" class="gt_row gt_right">2.511</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0022613</td>
## <td headers="Description" class="gt_row gt_left">ribonucleoprotein complex biogenesis</td>
## <td headers="p.adjust" class="gt_row gt_right">1.67 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">7</td>
## <td headers="geneID" class="gt_row gt_left">mtrex/prpf31/srpk1b/denr/imp4/nom1/mcts1</td>
## <td headers="log_padj" class="gt_row gt_right">1.778</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0043161</td>
## <td headers="Description" class="gt_row gt_left">proteasome-mediated ubiquitin-dependent protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">2.55 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">6</td>
## <td headers="geneID" class="gt_row gt_left">spsb1/psmc4/nsfl1c/psmd4a/psmd6/psmd1</td>
## <td headers="log_padj" class="gt_row gt_right">1.594</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0010498</td>
## <td headers="Description" class="gt_row gt_left">proteasomal protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">4.05 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">6</td>
## <td headers="geneID" class="gt_row gt_left">spsb1/psmc4/nsfl1c/psmd4a/psmd6/psmd1</td>
## <td headers="log_padj" class="gt_row gt_right">1.392</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0006002</td>
## <td headers="Description" class="gt_row gt_left">fructose 6-phosphate metabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">4.05 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_left">gfpt1/pfkpa</td>
## <td headers="log_padj" class="gt_row gt_right">1.392</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0022618</td>
## <td headers="Description" class="gt_row gt_left">protein-RNA complex assembly</td>
## <td headers="p.adjust" class="gt_row gt_right">6.08 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_left">prpf31/srpk1b/denr/mcts1</td>
## <td headers="log_padj" class="gt_row gt_right">1.216</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0071826</td>
## <td headers="Description" class="gt_row gt_left">protein-RNA complex organization</td>
## <td headers="p.adjust" class="gt_row gt_right">6.22 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_left">prpf31/srpk1b/denr/mcts1</td>
## <td headers="log_padj" class="gt_row gt_right">1.206</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0033617</td>
## <td headers="Description" class="gt_row gt_left">mitochondrial cytochrome c oxidase assembly</td>
## <td headers="p.adjust" class="gt_row gt_right">6.30 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_left">uqcc6/sco2</td>
## <td headers="log_padj" class="gt_row gt_right">1.201</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0006260</td>
## <td headers="Description" class="gt_row gt_left">DNA replication</td>
## <td headers="p.adjust" class="gt_row gt_right">6.30 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_left">rfc3/recql4/rmi1/ssbp1</td>
## <td headers="log_padj" class="gt_row gt_right">1.201</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0008535</td>
## <td headers="Description" class="gt_row gt_left">respiratory chain complex IV assembly</td>
## <td headers="p.adjust" class="gt_row gt_right">6.47 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_left">uqcc6/sco2</td>
## <td headers="log_padj" class="gt_row gt_right">1.189</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0007005</td>
## <td headers="Description" class="gt_row gt_left">mitochondrion organization</td>
## <td headers="p.adjust" class="gt_row gt_right">7.65 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">5</td>
## <td headers="geneID" class="gt_row gt_left">bbc3/uqcc6/sco2/ssbp1/lonp1</td>
## <td headers="log_padj" class="gt_row gt_right">1.116</td></tr>
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##     <tr class="gt_col_headings">
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="KEGG ID">KEGG ID</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Pathway Description">Pathway Description</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Adjusted P-value">Adjusted P-value</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Gene Count">Gene Count</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Gene IDs">Gene IDs</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="-log10(adj. P-value)">-log10(adj. P-value)</th>
##     </tr>
##   </thead>
##   <tbody class="gt_table_body">
##     <tr><td headers="ID" class="gt_row gt_left">dre03050</td>
## <td headers="Description" class="gt_row gt_left">Proteasome</td>
## <td headers="p.adjust" class="gt_row gt_right">8.10 × 10<sup style='font-size: 65%;'>−7</sup></td>
## <td headers="Count" class="gt_row gt_right">6</td>
## <td headers="geneID" class="gt_row gt_right">326884/415202/393261/554967/393518/393544</td>
## <td headers="log_padj" class="gt_row gt_right">6.091</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre03430</td>
## <td headers="Description" class="gt_row gt_left">Mismatch repair</td>
## <td headers="p.adjust" class="gt_row gt_right">3.61 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_right">259256/550504</td>
## <td headers="log_padj" class="gt_row gt_right">1.443</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre01230</td>
## <td headers="Description" class="gt_row gt_left">Biosynthesis of amino acids</td>
## <td headers="p.adjust" class="gt_row gt_right">3.61 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">386951/560827/560944</td>
## <td headers="log_padj" class="gt_row gt_right">1.443</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre05132</td>
## <td headers="Description" class="gt_row gt_left">Salmonella infection</td>
## <td headers="p.adjust" class="gt_row gt_right">3.61 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">5</td>
## <td headers="geneID" class="gt_row gt_right">100006250/336681/393154/327026/641421</td>
## <td headers="log_padj" class="gt_row gt_right">1.443</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre00030</td>
## <td headers="Description" class="gt_row gt_left">Pentose phosphate pathway</td>
## <td headers="p.adjust" class="gt_row gt_right">4.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_right">560827/560944</td>
## <td headers="log_padj" class="gt_row gt_right">1.390</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre03030</td>
## <td headers="Description" class="gt_row gt_left">DNA replication</td>
## <td headers="p.adjust" class="gt_row gt_right">4.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_right">259256/550504</td>
## <td headers="log_padj" class="gt_row gt_right">1.390</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre04540</td>
## <td headers="Description" class="gt_row gt_left">Gap junction</td>
## <td headers="p.adjust" class="gt_row gt_right">4.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">336681/393154/641421</td>
## <td headers="log_padj" class="gt_row gt_right">1.390</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre03082</td>
## <td headers="Description" class="gt_row gt_left">ATP-dependent chromatin remodeling</td>
## <td headers="p.adjust" class="gt_row gt_right">4.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">406357/393225/394048</td>
## <td headers="log_padj" class="gt_row gt_right">1.390</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre01200</td>
## <td headers="Description" class="gt_row gt_left">Carbon metabolism</td>
## <td headers="p.adjust" class="gt_row gt_right">4.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">386951/560827/560944</td>
## <td headers="log_padj" class="gt_row gt_right">1.390</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">dre04145</td>
## <td headers="Description" class="gt_row gt_left">Phagosome</td>
## <td headers="p.adjust" class="gt_row gt_right">7.95 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">336681/393154/641421</td>
## <td headers="log_padj" class="gt_row gt_right">1.100</td></tr>
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##   
##   
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##     </tr>
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##       <td colspan="2" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Parasite Exposure Response</td>
##     </tr>
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##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Metric">Metric</th>
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##     <tr><td headers="Metric" class="gt_row gt_left">Total significant gene-taxa correlations</td>
## <td headers="Value" class="gt_row gt_right">80</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Unique genes involved</td>
## <td headers="Value" class="gt_row gt_right">76</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Unique taxa involved</td>
## <td headers="Value" class="gt_row gt_right">8</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Enriched GO terms</td>
## <td headers="Value" class="gt_row gt_right">13</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Enriched KEGG pathways</td>
## <td headers="Value" class="gt_row gt_right">10</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Mean absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.484</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Median absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.467</td></tr>
##   </tbody>
##   
##   
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## </div>

Question 2: Historical Contingency of Parasite Response

## 
## === COMPREHENSIVE ENRICHMENT ANALYSIS - QUESTION 2: HISTORICAL CONTINGENCY OF PARASITE RESPONSE ===

## 
## === Creating comprehensive enrichment tables for: Historical_Contingency_Parasite_Response ===
## Number of unique genes: 2

## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## --> No gene can be mapped....

## --> Expected input gene ID: 336606,553401,393668,393461,368912,450002

## --> return NULL...

## $comprehensive_table
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## </style>
##   <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
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##       <td colspan="11" class="gt_heading gt_title gt_font_normal" style>Top 20 Significant Gene-Taxa Correlations</td>
##     </tr>
##     <tr class="gt_heading">
##       <td colspan="11" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Historical Contingency of Parasite Response</td>
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##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Analysis">Analysis</th>
##       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene ID">Gene ID</th>
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##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Parasite_Response</td>
## <td headers="gene_id" class="gt_row gt_left">lsm14b</td>
## <td headers="gene_description" class="gt_row gt_left">LSM family member 14B</td>
## <td headers="taxa_id" class="gt_row gt_left">Polymorphobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.377</td>
## <td headers="p_value" class="gt_row gt_right">9.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">3.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.377</td>
## <td headers="entrez_id" class="gt_row gt_right">393140</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Parasite_Response</td>
## <td headers="gene_id" class="gt_row gt_left">zranb2</td>
## <td headers="gene_description" class="gt_row gt_left">zinc finger, RAN-binding domain containing 2</td>
## <td headers="taxa_id" class="gt_row gt_left">Uliginosibacterium</td>
## <td headers="correlation" class="gt_row gt_right">0.627</td>
## <td headers="p_value" class="gt_row gt_right">2.40 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">2.40 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.627</td>
## <td headers="entrez_id" class="gt_row gt_right">406716</td>
## <td headers="go_terms" class="gt_row gt_left">defense response to bacterium; response to bacterium; defense response to other organism; response to external biotic stimulus; response to other organism; response to biotic stimulus; defense response</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
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##   
##   
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## 
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##       <td colspan="6" class="gt_heading gt_title gt_font_normal" style>Gene Ontology Enrichment Results</td>
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##     <tr><td headers="ID" class="gt_row gt_left">GO:0042742</td>
## <td headers="Description" class="gt_row gt_left">defense response to bacterium</td>
## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">1</td>
## <td headers="geneID" class="gt_row gt_left">zranb2</td>
## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0009617</td>
## <td headers="Description" class="gt_row gt_left">response to bacterium</td>
## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">1</td>
## <td headers="geneID" class="gt_row gt_left">zranb2</td>
## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0098542</td>
## <td headers="Description" class="gt_row gt_left">defense response to other organism</td>
## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">1</td>
## <td headers="geneID" class="gt_row gt_left">zranb2</td>
## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0043207</td>
## <td headers="Description" class="gt_row gt_left">response to external biotic stimulus</td>
## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">1</td>
## <td headers="geneID" class="gt_row gt_left">zranb2</td>
## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0051707</td>
## <td headers="Description" class="gt_row gt_left">response to other organism</td>
## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">1</td>
## <td headers="geneID" class="gt_row gt_left">zranb2</td>
## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
##     <tr><td headers="ID" class="gt_row gt_left">GO:0009607</td>
## <td headers="Description" class="gt_row gt_left">response to biotic stimulus</td>
## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
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##     <tr><td headers="ID" class="gt_row gt_left">GO:0006952</td>
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## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
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## <td headers="geneID" class="gt_row gt_left">zranb2</td>
## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
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##       <td colspan="2" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Historical Contingency of Parasite Response</td>
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## <td headers="Value" class="gt_row gt_right">2</td></tr>
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## <td headers="Value" class="gt_row gt_right">2</td></tr>
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## <td headers="Value" class="gt_row gt_right">2</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Enriched GO terms</td>
## <td headers="Value" class="gt_row gt_right">7</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Enriched KEGG pathways</td>
## <td headers="Value" class="gt_row gt_right">0</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Mean absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.502</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Median absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.502</td></tr>
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##   
##   
## </table>
## </div>

Question 3: Historical Contingency of Recovery

## 
## === COMPREHENSIVE ENRICHMENT ANALYSIS - QUESTION 3: HISTORICAL CONTINGENCY OF RECOVERY ===

## 
## === Creating comprehensive enrichment tables for: Historical_Contingency_Recovery ===
## Number of unique genes: 63

## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

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##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100001589</td>
## <td headers="gene_description" class="gt_row gt_left">tripartite motif-containing protein 47-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Runella</td>
## <td headers="correlation" class="gt_row gt_right">−0.503</td>
## <td headers="p_value" class="gt_row gt_right">7.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">3.27 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.503</td>
## <td headers="entrez_id" class="gt_row gt_right">100001589</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100537840</td>
## <td headers="gene_description" class="gt_row gt_left">E3 ubiquitin-protein ligase rnf213-alpha-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Pirellula</td>
## <td headers="correlation" class="gt_row gt_right">−0.768</td>
## <td headers="p_value" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.15 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.768</td>
## <td headers="entrez_id" class="gt_row gt_right">100537840</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100537840</td>
## <td headers="gene_description" class="gt_row gt_left">E3 ubiquitin-protein ligase rnf213-alpha-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Reyranella</td>
## <td headers="correlation" class="gt_row gt_right">−0.340</td>
## <td headers="p_value" class="gt_row gt_right">5.69 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">8.92 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.340</td>
## <td headers="entrez_id" class="gt_row gt_right">100537840</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100537840</td>
## <td headers="gene_description" class="gt_row gt_left">E3 ubiquitin-protein ligase rnf213-alpha-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Runella</td>
## <td headers="correlation" class="gt_row gt_right">−0.476</td>
## <td headers="p_value" class="gt_row gt_right">5.59 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">8.92 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.476</td>
## <td headers="entrez_id" class="gt_row gt_right">100537840</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882702</td>
## <td headers="gene_description" class="gt_row gt_left">golgin subfamily A member 6-like protein 22</td>
## <td headers="taxa_id" class="gt_row gt_left">Neochlamydia</td>
## <td headers="correlation" class="gt_row gt_right">−0.390</td>
## <td headers="p_value" class="gt_row gt_right">6.39 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">9.62 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.390</td>
## <td headers="entrez_id" class="gt_row gt_right">101882702</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882702</td>
## <td headers="gene_description" class="gt_row gt_left">golgin subfamily A member 6-like protein 22</td>
## <td headers="taxa_id" class="gt_row gt_left">Roseomonas</td>
## <td headers="correlation" class="gt_row gt_right">−0.555</td>
## <td headers="p_value" class="gt_row gt_right">5.79 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.37 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.555</td>
## <td headers="entrez_id" class="gt_row gt_right">101882702</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882847</td>
## <td headers="gene_description" class="gt_row gt_left">rho-associated protein kinase 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Pseudoxanthomonas</td>
## <td headers="correlation" class="gt_row gt_right">0.901</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">1.69 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.901</td>
## <td headers="entrez_id" class="gt_row gt_right">101882847</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101883008</td>
## <td headers="gene_description" class="gt_row gt_left">zinc finger protein 664-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.424</td>
## <td headers="p_value" class="gt_row gt_right">4.70 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">6.68 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.424</td>
## <td headers="entrez_id" class="gt_row gt_right">101883008</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101883092</td>
## <td headers="gene_description" class="gt_row gt_left">interferon-induced very large GTPase 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Pirellula</td>
## <td headers="correlation" class="gt_row gt_right">−0.709</td>
## <td headers="p_value" class="gt_row gt_right">2.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.76 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.709</td>
## <td headers="entrez_id" class="gt_row gt_right">101883092</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101883092</td>
## <td headers="gene_description" class="gt_row gt_left">interferon-induced very large GTPase 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Runella</td>
## <td headers="correlation" class="gt_row gt_right">−0.508</td>
## <td headers="p_value" class="gt_row gt_right">2.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.76 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.508</td>
## <td headers="entrez_id" class="gt_row gt_right">101883092</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC103910189</td>
## <td headers="gene_description" class="gt_row gt_left">interferon-induced very large GTPase 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Pirellula</td>
## <td headers="correlation" class="gt_row gt_right">−0.681</td>
## <td headers="p_value" class="gt_row gt_right">2.40 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.76 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.681</td>
## <td headers="entrez_id" class="gt_row gt_right">103910189</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC103910189</td>
## <td headers="gene_description" class="gt_row gt_left">interferon-induced very large GTPase 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Runella</td>
## <td headers="correlation" class="gt_row gt_right">−0.471</td>
## <td headers="p_value" class="gt_row gt_right">2.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.76 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.471</td>
## <td headers="entrez_id" class="gt_row gt_right">103910189</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC108183350</td>
## <td headers="gene_description" class="gt_row gt_left">protein NLRC3-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Luteolibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.369</td>
## <td headers="p_value" class="gt_row gt_right">3.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.03 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.369</td>
## <td headers="entrez_id" class="gt_row gt_right">108183350</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC110438438</td>
## <td headers="gene_description" class="gt_row gt_left">serine/threonine-protein kinase 38-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Pseudoxanthomonas</td>
## <td headers="correlation" class="gt_row gt_right">0.539</td>
## <td headers="p_value" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.539</td>
## <td headers="entrez_id" class="gt_row gt_right">110438438</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC110439307</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC110439307</td>
## <td headers="taxa_id" class="gt_row gt_left">Pseudoxanthomonas</td>
## <td headers="correlation" class="gt_row gt_right">−0.661</td>
## <td headers="p_value" class="gt_row gt_right">8.19 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">9.70 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.661</td>
## <td headers="entrez_id" class="gt_row gt_right">110439307</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137487211</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137487211</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.378</td>
## <td headers="p_value" class="gt_row gt_right">5.89 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.37 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.378</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137487211</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137487211</td>
## <td headers="taxa_id" class="gt_row gt_left">Flavobacterium</td>
## <td headers="correlation" class="gt_row gt_right">−0.731</td>
## <td headers="p_value" class="gt_row gt_right">3.90 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">6.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.731</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137489722</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137489722</td>
## <td headers="taxa_id" class="gt_row gt_left">Pseudoxanthomonas</td>
## <td headers="correlation" class="gt_row gt_right">0.979</td>
## <td headers="p_value" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">6.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.979</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137496053</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137496053</td>
## <td headers="taxa_id" class="gt_row gt_left">Gemmobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.463</td>
## <td headers="p_value" class="gt_row gt_right">1.10 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.34 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.463</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##     <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137496184</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137496184</td>
## <td headers="taxa_id" class="gt_row gt_left">Bosea</td>
## <td headers="correlation" class="gt_row gt_right">−0.522</td>
## <td headers="p_value" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.15 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.522</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
##   </tbody>
##   
##   
## </table>
## </div>
## 
## $kegg_table
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##     <tr><td headers="ID" class="gt_row gt_left">dre04622</td>
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##     <tr><td headers="Metric" class="gt_row gt_left">Total significant gene-taxa correlations</td>
## <td headers="Value" class="gt_row gt_right">97</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Unique genes involved</td>
## <td headers="Value" class="gt_row gt_right">63</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Unique taxa involved</td>
## <td headers="Value" class="gt_row gt_right">24</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Enriched GO terms</td>
## <td headers="Value" class="gt_row gt_right">0</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Enriched KEGG pathways</td>
## <td headers="Value" class="gt_row gt_right">4</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Mean absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.586</td></tr>
##     <tr><td headers="Metric" class="gt_row gt_left">Median absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.572</td></tr>
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Results Export

Summary and Conclusions

## 
## === INTEGRATED ANALYSIS SUMMARY ===

Integrated Analysis Summary
Gene-taxa correlations across three key research questions
Research Question	N Significant Correlations	N Significant Partial Correlations	N Unique Genes	N Unique Taxa
1. Parasite Exposure Response (A- T- P- vs A- T- P+)	11681	80	2368	22
2. Historical Contingency of Parasite Response (A- T- P+ vs A+ T- P+ vs A- T+ P+ vs A+ T+ P+)	4	2	3	3
3. Historical Contingency of Recovery (A- T- P- vs A+ T- P- vs A- T+ P- vs A+ T+ P-)	144	97	78	30

## 
## === FILES SUMMARY ===

## Results directory: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results

## Configuration: LOAD_EXISTING_RESULTS = TRUE

## Analysis results were loaded from existing files.
## To recompute results, set LOAD_EXISTING_RESULTS = FALSE in the configuration section.

## 
## Available result files:
## - all_enrichment_results_20250707_115941.rds (modified: 2025-07-07 11:59:54 )
## - all_enrichment_results_20250707_120317.rds (modified: 2025-07-07 12:03:18 )
## - results_all_20250630_200742.rds (modified: 2025-06-30 20:08:10 )
## - results_historical_contingency_20250630_200918.rds (modified: 2025-06-30 20:09:18 )
## - results_parasite_20250630_200828.rds (modified: 2025-06-30 20:08:28 )
## - results_recovery_20250630_201002.rds (modified: 2025-06-30 20:10:02 )

```